"
],
"text/plain": [
" color director_name num_critic_for_reviews duration \\\n",
"0 Color James Cameron 723.0 178.0 \n",
"\n",
" director_facebook_likes actor_3_facebook_likes actor_2_name \\\n",
"0 0.0 855.0 Joel David Moore \n",
"\n",
" actor_1_facebook_likes gross genres \\\n",
"0 1000.0 760505847.0 Action|Adventure|Fantasy|Sci-Fi \n",
"\n",
" actor_1_name movie_title num_voted_users cast_total_facebook_likes \\\n",
"0 CCH Pounder Avatar 886204 4834 \n",
"\n",
" actor_3_name facenumber_in_poster num_user_for_reviews language country \\\n",
"0 Wes Studi 0.0 3054.0 English USA \n",
"\n",
" content_rating budget title_year actor_2_facebook_likes imdb_score \\\n",
"0 PG-13 237000000.0 2009.0 936.0 7.9 \n",
"\n",
" movie_facebook_likes \n",
"0 33000 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Drop unnecessary columns\n",
"columns = ['movie_imdb_link','plot_keywords','aspect_ratio']\n",
"df.drop(columns, inplace=True, axis=1)\n",
"df.head(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are 45 duplicate rows in the dataset. We'll drop these duplicated rows to avoid repeated information that could influence our results."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"45"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Check for duplicates\n",
"len(df[df.duplicated()])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4998"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Drop duplicates and check number of rows\n",
"df = df.drop_duplicates()\n",
"len(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below are all the values for each column that are null or missing. We'll have to deal with these null values in various ways because some methods of analysis will not allow null values."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"gross 874\n",
"budget 487\n",
"content_rating 301\n",
"title_year 107\n",
"director_facebook_likes 103\n",
"director_name 103\n",
"num_critic_for_reviews 49\n",
"actor_3_facebook_likes 23\n",
"actor_3_name 23\n",
"num_user_for_reviews 21\n",
"color 19\n",
"duration 15\n",
"facenumber_in_poster 13\n",
"actor_2_facebook_likes 13\n",
"actor_2_name 13\n",
"language 12\n",
"actor_1_name 7\n",
"actor_1_facebook_likes 7\n",
"country 5\n",
"dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Check for missing values\n",
"df.isnull().sum().sort_values(ascending=False).head(19)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The three variables missing the most data are gross, budget, and content rating. Because of the nature of these variables, it's difficult to find a solution to fill the null values. There is too much variation in the dataset to replace with mean, median, or mode. Therefore we'll have to drop all rows that have null values for these three variables."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"gross 874\n",
"budget 487\n",
"content_rating 301\n",
"dtype: int64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Check for missing values\n",
"df.isnull().sum().sort_values(ascending=False).head(3)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3806"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Drop null gross/budget\n",
"df.dropna(subset=['gross'], how='all', inplace = True)\n",
"df.dropna(subset=['budget'], how='all', inplace = True)\n",
"df.dropna(subset=['content_rating'], how='all', inplace = True)\n",
"len(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Even after dropping the null values in gross, budget, and content rating, there are still 3,806 observations in the dataset. This is still a sufficient sample size to analyze and draw conclusions from. Now there are still 11 columns that have null values."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"actor_3_facebook_likes 6\n",
"facenumber_in_poster 6\n",
"actor_3_name 6\n",
"color 2\n",
"actor_2_facebook_likes 2\n",
"language 2\n",
"actor_2_name 2\n",
"num_critic_for_reviews 1\n",
"actor_1_facebook_likes 1\n",
"actor_1_name 1\n",
"dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Check for missing values\n",
"df.isnull().sum().sort_values(ascending=False).head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One method of filling missing data, especially categorical data, is to replace the null value with the most commonly observed value."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Color 3680\n",
" Black and White 124\n",
"Name: color, dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Majority of movies are in color\n",
"df['color'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"English 3644\n",
"French 34\n",
"Spanish 24\n",
"Mandarin 14\n",
"German 11\n",
"Name: language, dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Majority of movies are in English\n",
"df['language'].value_counts().sort_values(ascending=False).head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the case of the color and language columns, each variable has one value that occurs more frequently than others. So we'll replace the missing color values with color, and the missing language values with English. Now only 8 columns have missing values."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"actor_3_facebook_likes 6\n",
"facenumber_in_poster 6\n",
"actor_3_name 6\n",
"actor_2_facebook_likes 2\n",
"actor_2_name 2\n",
"actor_1_name 1\n",
"num_critic_for_reviews 1\n",
"actor_1_facebook_likes 1\n",
"dtype: int64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Replace null values with the most popular value in a categorial columns\n",
"df = df.fillna({'color': 'Color'})\n",
"df = df.fillna({'language': 'English'})\n",
"\n",
"df.isnull().sum().sort_values(ascending=False).head(8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another technique for filling missing data is by calculating the central tendency, like mean or median, for each column and replacing the null values with that specific central tendency. This is the methodology we'll use for missing integer variables, number of faces in the movie poster, the number of reviews given by critics, and actors Facebook likes. Since there is some variation in the data, and since all the numbers are whole numbers, we'll fill the missing values with the median."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"num_critic_for_reviews 136.0\n",
"duration 106.0\n",
"director_facebook_likes 59.0\n",
"actor_3_facebook_likes 432.0\n",
"actor_1_facebook_likes 1000.0\n",
"gross 28749642.5\n",
"num_voted_users 52312.5\n",
"cast_total_facebook_likes 3951.0\n",
"facenumber_in_poster 1.0\n",
"num_user_for_reviews 205.0\n",
"budget 25000000.0\n",
"title_year 2005.0\n",
"actor_2_facebook_likes 670.0\n",
"imdb_score 6.6\n",
"movie_facebook_likes 218.0\n",
"dtype: float64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Median replace because of variation\n",
"df.median()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"actor_3_name 6\n",
"actor_2_name 2\n",
"actor_1_name 1\n",
"dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Replace null values with median\n",
"newposter = df['facenumber_in_poster'].median()\n",
"df = df.fillna({'facenumber_in_poster': newposter})\n",
"newcritic = df['num_critic_for_reviews'].median()\n",
"df = df.fillna({'num_critic_for_reviews': newcritic})\n",
"newact1 = df['actor_1_facebook_likes'].median()\n",
"df = df.fillna({'actor_1_facebook_likes': newact1})\n",
"newact2 = df['actor_2_facebook_likes'].median()\n",
"df = df.fillna({'actor_2_facebook_likes': newact2})\n",
"newact3 = df['actor_3_facebook_likes'].median()\n",
"df = df.fillna({'actor_3_facebook_likes': newact3})\n",
"df.isnull().sum().sort_values(ascending=False).head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After filling these values with the median three columns have null values: the names of actors one, two, and three. We could fill these missing values, but since there are numerous actors in the dataset, and since these few missing values won't affect our analysis, we'll leave these null values."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After filling null values, we'll want to handle any outliers in the data that might influence analysis. Since the median budget is 25 million dollars, some outrageously high budgets in the dataset are present and will be considered outliers. We need to remove these values that are either typos or inaccurate information since there are no movies that have had a budget even close to 12 billion dollars."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"25000000.0"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Median budget for context\n",
"df['budget'].median()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2988 12,215,500,000\n",
"3859 4,200,000,000\n",
"3005 2,500,000,000\n",
"2323 2,400,000,000\n",
"2334 2,127,519,898\n",
"Name: budget, dtype: float64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Check for outliers\n",
"pd.options.display.float_format = '{:,.0f}'.format\n",
"df['budget'].sort_values(ascending=False).head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"## Reset scientific notation\n",
"pd.reset_option('^display.', silent=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll treat any budget above 300 million dollars as an outlier, and drop the row. After doing so, 12 outliers were dropped from the dataset."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3794"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Drop outliers\n",
"df.drop( df[ df['budget'] >= 300000001 ].index , inplace=True)\n",
"len(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll sort out the content rating category. There are 12 different options in the content rating column. We can consolidate these ratings and organize them into more manageable categories."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"R 1715\n",
"PG-13 1311\n",
"PG 572\n",
"G 91\n",
"Not Rated 42\n",
"Unrated 24\n",
"Approved 17\n",
"X 10\n",
"NC-17 6\n",
"Passed 3\n",
"M 2\n",
"GP 1\n",
"Name: content_rating, dtype: int64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Check content ratings\n",
"df['content_rating'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll redistribute the content rating categories the following way:\n",
"* GP -> PG \n",
"* Approved -> PG\n",
"* Passed -> PG\n",
"* M -> R\n",
"* Not Rated -> Unrated\n",
"* X -> Unrated\n",
"* NC-17 -> Unrated\n",
"\n",
"Now we have 5 different content ratings that follow a natural scale, from movies that are more family friendly to movies that have mature themes."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"R 1717\n",
"PG-13 1311\n",
"PG 593\n",
"G 91\n",
"Unrated 82\n",
"Name: content_rating, dtype: int64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Redistribute into more managable groups\n",
"df = df.replace('GP', 'PG')\n",
"df = df.replace('Approved', 'PG')\n",
"df = df.replace('Passed', 'PG')\n",
"df = df.replace('M', 'R')\n",
"df = df.replace('Not Rated', 'Unrated')\n",
"df = df.replace('X', 'Unrated')\n",
"df = df.replace('NC-17', 'Unrated')\n",
"df['content_rating'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to analyze categorical columns with certain methodologies, we'll have to convert those columns from objects to integers. We'll do this for the following columns; color, language, country, and content rating. For color, we'll convert the column into dummy variables since there are only two options for the color column. The movie is either in black and white, which is assigned a 0, or in color, which is assigned a 1."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1 3670\n",
"0 124\n",
"Name: color, dtype: int64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Convert color to dummy variable\n",
"df = pd.get_dummies(df, columns=['color'])\n",
"df = df.drop(['color_ Black and White'], axis=1)\n",
"df = df.rename(columns={'color_Color':'color'})\n",
"df['color'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we already redistributed the content rating column into 5 groups that follow a natural scale, we can assign each of the ratings a number. G will be 1, PG will be 2, etc. In other words, a 1 will be a more family friendly movie, and a 5 will have more adult themes."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4 1717\n",
"3 1311\n",
"2 593\n",
"1 91\n",
"5 82\n",
"Name: content_rating, dtype: int64"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Remap content rating from categorical to integer\n",
"df['content_rating'] = df['content_rating'].map({'G': 1, 'PG': 2, 'PG-13':3, 'R': 4, 'Unrated': 5})\n",
"df['content_rating'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We've already established that the categorical columns language and country have a logical most frequent value: English is the most occurring language and the United States is the most common country. Therefore, we can create a new column with dummy variables for each of these categories. For language, 1 means the movie is in English, 0 means the movie is in a different language. For country, 1 means the movie is from the United States, and 0 means the movie is outside the USA."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0 3645\n",
"0.0 149\n",
"Name: in_english, dtype: int64"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Remap language and country to dummy variables\n",
"df.loc[df.language == 'English', 'in_english'] = 1 \n",
"df.loc[df.language != 'English', 'in_english'] = 0\n",
"df.loc[df.country == 'USA', 'from_USA'] = 1 \n",
"df.loc[df.country != 'USA', 'from_USA'] = 0\n",
"df['in_english'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0 3025\n",
"0.0 769\n",
"Name: from_USA, dtype: int64"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['from_USA'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we need to focus on the genres column. Since it's difficult to sum a single movie up into just one genre, many movies list multiple genres. We'll have to split each movie's listed genres so we can analyze them individually."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Comedy|Drama|Romance 150\n",
"Drama 147\n",
"Comedy 143\n",
"Comedy|Drama 142\n",
"Comedy|Romance 136\n",
"Name: genres, dtype: int64"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Check genres in dataframe\n",
"df['genres'].value_counts().head()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"
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" \n",
" \n",
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"text/plain": [
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"2 Action|Adventure|Thriller 6.8"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Create a new data frame\n",
"gen = df[['genres','imdb_score']]\n",
"gen.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3794"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Check number of rows\n",
"len(gen)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we're splitting up the genres, each movie could have multiple rows depending on the number of genres listed. For instance, if a certain movie has 5 genres listed, that movie will have 5 separate rows. For this reason, there are 11,325 rows after splitting the genres."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Split genres into multiple rows\n",
"spl = pd.DataFrame(gen.genres.str.split('|').tolist(), index=gen.imdb_score).stack()\n",
"spl = spl.reset_index()[[0, 'imdb_score']]\n",
"spl.columns = ['genres', 'imdb_score']\n",
"spl.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"11281"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(spl)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It may provide some insight to create a new column based on gross and budget. Subtracting budget from gross will yield the total profit of the movie."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['director_name', 'num_critic_for_reviews', 'duration',\n",
" 'director_facebook_likes', 'actor_3_facebook_likes', 'actor_2_name',\n",
" 'actor_1_facebook_likes', 'gross', 'genres', 'actor_1_name',\n",
" 'movie_title', 'num_voted_users', 'cast_total_facebook_likes',\n",
" 'actor_3_name', 'facenumber_in_poster', 'num_user_for_reviews',\n",
" 'language', 'country', 'content_rating', 'budget', 'title_year',\n",
" 'actor_2_facebook_likes', 'imdb_score', 'movie_facebook_likes', 'color',\n",
" 'in_english', 'from_USA', 'profit'],\n",
" dtype='object')"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Calculate profit column\n",
"df['profit'] = df['gross'] - df['budget']\n",
"df.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For analysis later in the project, we'll set custom bins based on the IMDb score of the movie. The movies in each bin represent the following:\n",
"* Bin 1: 'bad' movies\n",
"* Bin 2: 'ok' movies\n",
"* Bin 3: 'good' movies\n",
"* Bin 4: 'excellent' movies"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1 95\n",
"2 1067\n",
"3 2476\n",
"4 156\n",
"Name: movie_quality, dtype: int64"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Set bins\n",
"df['movie_quality'] = pd.cut(df['imdb_score'], [0, 4, 6, 8, 10], labels=['1', '2', '3', '4'])\n",
"df['movie_quality'].value_counts().sort_index()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll reorder the columns so that similar variables are next to each other for easier analysis. We'll also create a copy of the dataframe with only integer columns for use later in the project."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
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imdb_score
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movie_quality
\n",
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movie_title
\n",
"
title_year
\n",
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content_rating
\n",
"
duration
\n",
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country
\n",
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from_USA
\n",
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language
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in_english
\n",
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...
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movie_facebook_likes
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director_facebook_likes
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actor_1_facebook_likes
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actor_2_facebook_likes
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actor_3_facebook_likes
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cast_total_facebook_likes
\n",
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num_critic_for_reviews
\n",
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num_user_for_reviews
\n",
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num_voted_users
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facenumber_in_poster
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"
\n",
" \n",
" \n",
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0
\n",
"
7.9
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3
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Avatar
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2009.0
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3
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178.0
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USA
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1.0
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English
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1.0
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33000
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1000.0
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1 rows × 28 columns
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"text/plain": [
" imdb_score movie_quality movie_title title_year content_rating duration \\\n",
"0 7.9 3 Avatar 2009.0 3 178.0 \n",
"\n",
" country from_USA language in_english ... movie_facebook_likes \\\n",
"0 USA 1.0 English 1.0 ... 33000 \n",
"\n",
" director_facebook_likes actor_1_facebook_likes actor_2_facebook_likes \\\n",
"0 0.0 1000.0 936.0 \n",
"\n",
" actor_3_facebook_likes cast_total_facebook_likes num_critic_for_reviews \\\n",
"0 855.0 4834 723.0 \n",
"\n",
" num_user_for_reviews num_voted_users facenumber_in_poster \n",
"0 3054.0 886204 0.0 \n",
"\n",
"[1 rows x 28 columns]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Reorder columns\n",
"df = df[['imdb_score', 'movie_quality', 'movie_title', 'title_year', 'content_rating', 'duration', 'country', 'from_USA', 'language', 'in_english', 'color', 'gross', 'budget', 'profit', 'director_name', 'actor_1_name', 'actor_2_name', 'actor_3_name', 'movie_facebook_likes', 'director_facebook_likes', 'actor_1_facebook_likes', 'actor_2_facebook_likes', 'actor_3_facebook_likes', 'cast_total_facebook_likes', 'num_critic_for_reviews', 'num_user_for_reviews', 'num_voted_users', 'facenumber_in_poster']]\n",
"df.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Int64Index: 3794 entries, 0 to 5042\n",
"Data columns (total 21 columns):\n",
"imdb_score 3794 non-null float64\n",
"movie_quality 3794 non-null category\n",
"title_year 3794 non-null float64\n",
"content_rating 3794 non-null int64\n",
"duration 3794 non-null float64\n",
"from_USA 3794 non-null float64\n",
"in_english 3794 non-null float64\n",
"color 3794 non-null uint8\n",
"gross 3794 non-null float64\n",
"budget 3794 non-null float64\n",
"profit 3794 non-null float64\n",
"movie_facebook_likes 3794 non-null int64\n",
"director_facebook_likes 3794 non-null float64\n",
"actor_1_facebook_likes 3794 non-null float64\n",
"actor_2_facebook_likes 3794 non-null float64\n",
"actor_3_facebook_likes 3794 non-null float64\n",
"cast_total_facebook_likes 3794 non-null int64\n",
"num_critic_for_reviews 3794 non-null float64\n",
"num_user_for_reviews 3794 non-null float64\n",
"num_voted_users 3794 non-null int64\n",
"facenumber_in_poster 3794 non-null float64\n",
"dtypes: category(1), float64(15), int64(4), uint8(1)\n",
"memory usage: 600.4 KB\n"
]
}
],
"source": [
"## New dataframe for integer columns\n",
"df1 = df.drop(['movie_title', 'country', 'language', 'director_name', 'actor_1_name', 'actor_2_name', 'actor_3_name'], axis =1)\n",
"df1.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Data Visualization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"While the ultimate goal of this analysis is to predict the IMDb score of certain movies, it may help to first get a better understanding of our data through visualization."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### a. Movie Characterisitics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This first figure shows the distribution of the IMDb scores in the form of a box plot. The median IMDb score is 6.6, the first quartile is a score of 5.9, and the third quartile is 7.2."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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"The World Is Not Enough ",
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"The BFG ",
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"Road to Perdition ",
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"Cold Mountain ",
"The Book of Eli ",
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"The Haunting ",
"Space Jam ",
"The Pink Panther ",
"The Day the Earth Stood Still ",
"Conspiracy Theory ",
"Fury ",
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"Timeline ",
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"Babe: Pig in the City ",
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"Children of Men ",
"X-Men ",
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"The Rock ",
"Ice Age: The Meltdown ",
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"Be Cool ",
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"Tears of the Sun ",
"Killers ",
"The Man from U.N.C.L.E. ",
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"Bandits ",
"First Knight ",
"Anna and the King ",
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"Hostage ",
"Titan A.E. ",
"Hollywood Homicide ",
"Soldier ",
"Monkeybone ",
"Flight of the Phoenix ",
"Unbreakable ",
"Minions ",
"Sucker Punch ",
"Snake Eyes ",
"Sphere ",
"The Angry Birds Movie ",
"Fool's Gold ",
"Funny People ",
"The Kingdom ",
"Talladega Nights: The Ballad of Ricky Bobby ",
"Dr. Dolittle 2 ",
"Braveheart ",
"Jarhead ",
"The Simpsons Movie ",
"The Majestic ",
"Driven ",
"Two Brothers ",
"The Village ",
"Doctor Dolittle ",
"Signs ",
"Shrek 2 ",
"Cars ",
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5.2,
8.1,
6.6,
6.2,
7.2,
7.3,
6.7,
6.4,
7.8,
6.4,
4.1,
4.1,
7.4,
5.8,
7.6,
7.2,
7.8,
7.7,
6.4,
5.1,
5.5,
7.4,
6,
7.5,
7,
7.5,
7.3,
5.7,
7.3,
7.2,
5.9,
7.8,
7.7,
8.1,
6.6,
7.1,
5.9,
8,
4.6,
6.1,
6.4,
6,
5.2,
7.6,
6.4,
6.1,
6.1,
5.2,
7.7,
7.3,
6.9,
8.5,
6.3,
5.9,
7.8,
6.7,
6.4,
5.9,
6.6,
6.8,
6.5,
6.6,
5.8,
6.9,
7.1,
5.8,
7.2,
6,
4.7,
5.2,
5.5,
7,
5.8,
6.2,
6.5,
7.2,
5.1,
4.7,
5.9,
5.8,
7.2,
6.2,
5.7,
6.1,
6,
6.9,
6.5,
5,
5.7,
7,
5.1,
5.3,
4.4,
4.7,
6.7,
6.7,
5.7,
7.4,
6.1,
6.4,
6.2,
6.2,
5.9,
4,
6.2,
4.6,
6.4,
5.9,
5.1,
7.6,
4.2,
7.8,
5.8,
5.9,
8.4,
4.8,
6.2,
6.5,
6.3,
3.3,
5.9,
5.8,
4.7,
4.1,
6.8,
6.2,
4.5,
5.8,
7.3,
5.9,
4.4,
5.8,
5.1,
6.9,
6.2,
6.9,
7.3,
7.1,
6,
7,
7.6,
7.1,
6.7,
7,
8,
5.3,
4.9,
6.4,
6.1,
6.5,
5.7,
5.1,
6.6,
6.5,
6.9,
7.6,
5.6,
6.2,
4.4,
5.6,
5.5,
6.7,
6.1,
6.2,
7.3,
6.6,
8.2,
6.4,
6.4,
5.2,
6.5,
7.1,
7.3,
5.2,
7.7,
7.6,
5.7,
7,
6,
8.1,
8,
5.6,
6.1,
6.9,
5.2,
7,
6.3,
7,
6.9,
6.2,
6.4,
6.4,
5.7,
6.1,
5.4,
6.7,
6.8,
6,
7.8,
5.3,
4.5,
5.4,
7.8,
7.2,
6.6,
7.6,
5.9,
6.7,
7.7,
5.4,
6.9,
7.7,
6.8,
6.4,
5.7,
7.3,
6.8,
6.3,
5.9,
7.4,
8.3,
6.2,
6.3,
5.8,
7.5,
6.3,
6.4,
7.2,
6.3,
6.9,
6.6,
6,
7.5,
7.7,
6.2,
5.4,
6.6,
5.3,
5.6,
5.9,
7.8,
6.7,
7.4,
6.2,
5.4,
6.7,
5.3,
5.9,
4.8,
3.8,
8.5,
6.8,
5.3,
7.3,
6.6,
6.2,
5.2,
6.2,
6.2,
6.6,
6.2,
5.1,
6.6,
6.1,
6.6,
5.9,
6.3,
7.1,
5,
5.6,
7.4,
4.5,
6.2,
5,
6.5,
5.1,
6.5,
6.2,
6.3,
3.8,
6.2,
5.7,
6.7,
6.8,
6,
7.3,
5.5,
6.7,
4.8,
5.7,
5.1,
6,
4.2,
7.4,
4.6,
6.9,
6.9,
8,
6.4,
6.3,
6.8,
6.8,
5.4,
7.2,
7.3,
5.2,
5.5,
7.7,
7.1,
5.3,
5.6,
5.7,
7.1,
7.6,
5.5,
5.1,
4.9,
6.5,
5.6,
5.3,
6.5,
6.8,
6.5,
6,
8.4,
6,
7.6,
6.9,
6.4,
5.1,
7,
5.7,
6.8,
6.7,
6.2,
7.2,
6.2,
5.6,
4.4,
7.5,
7.1,
6.4,
7.1,
6.9,
7.5,
6.3,
6.4,
5.9,
6.8,
6.3,
3.6,
5.3,
5.9,
6.9,
6.9,
6.1,
8.5,
6.3,
7.3,
6.3,
7.2,
7.3,
6.3,
8.1,
6.9,
6.3,
7.3,
5.5,
6.1,
6.9,
7.2,
6.4,
6.4,
8.3,
7.2,
6.8,
6.5,
7.8,
7.6,
7.2,
6.7,
6.8,
6.3,
6.2,
6.2,
8.6,
8,
7,
8,
8.1,
6.7,
7.9,
6.1,
4.2,
6.1,
6.6,
7.5,
7.4,
7.2,
6.9,
7.4,
5.4,
6.8,
6.3,
7.2,
6.9,
6,
5.9,
5.4,
5.9,
6.1,
7.7,
5.8,
7.6,
6.1,
5.4,
5.1,
6.4,
6.3,
7.5,
7.1,
7.8,
6.5,
6.6,
7.4,
7.6,
7.5,
6.6,
7.2,
7.6,
6.2,
5.6,
7.6,
6.6,
7,
2.7,
7.6,
6.6,
6.9,
6.8,
3.7,
6.1,
5.9,
6.7,
6.9,
5.5,
7.1,
7.1,
7.3,
3.4,
6.8,
6.9,
7,
5.5,
5.1,
6.2,
5.9,
5.2,
6.2,
5.5,
7.4,
4.4,
6.3,
6.1,
5.3,
5.4,
6.7,
5.9,
7.3,
5.5,
5.8,
4.6,
6.7,
5.1,
5.6,
7,
6.4,
6.7,
4.1,
5.5,
2.7,
6.4,
4.8,
6.1,
4.8,
7,
6.8,
5.6,
6.1,
7.9,
8.4,
6.5,
7.1,
6.6,
7,
5.6,
4.8,
7.5,
6,
6.8,
6.5,
7.9,
6.4,
5.8,
7.7,
5.3,
5.3,
7.5,
6.9,
4.9,
7.1,
8,
7.9,
7.6,
5.9,
6.3,
6.4,
8.2,
6.9,
7.8,
6.7,
7.5,
7.4,
5.2,
7.6,
7.3,
6.6,
6.8,
6.9,
5.8,
6.6,
6.7,
6.7,
6.3,
7.7,
6.1,
4.9,
6.2,
7.8,
8.2,
6.9,
6.2,
6.9,
4.8,
8,
5.3,
6.7,
5.4,
5.4,
4.9,
6.1,
5.8,
7,
6.5,
6.6,
5.7,
6.6,
7,
7.4,
5.3,
7.4,
7.4,
6.8,
7.2,
6,
7.8,
6.6,
7.9,
5.7,
7.1,
5.6,
7.8,
7.9,
6.9,
7.7,
6.9,
6,
6.2,
5.9,
6.8,
3.6,
6.7,
6.3,
6.4,
6.4,
5.7,
6.2,
5.2,
6.1,
7.1,
7.2,
6.5,
6,
7,
7,
7.5,
6.6,
7.4,
6.5,
6.2,
7.8,
5.2,
6.5,
6.5,
5.2,
7.2,
7.1,
4.5,
5.7,
6,
6.4,
5.2,
4.3,
6.1,
6.8,
5.2,
6.5,
7.5,
7.1,
6.9,
8,
8.2,
6.4,
7.9,
6.7,
6.1,
8.9,
8.1,
6.2,
4.9,
5.8,
6,
7,
6,
7.9,
8.1,
6.2,
6.7,
7.3,
4.6,
6.1,
6.2,
7.8,
6.1,
5.8,
6.5,
7.2,
7.8,
4.7,
6.8,
5.9,
7.2,
8.7,
5,
6.6,
8.3,
6.7,
7.8,
6.5,
6.1,
8.1,
5.2,
5.6,
5.8,
6.6,
6.6,
5.5,
7,
6.5,
5.8,
5.6,
5.6,
5.8,
7.6,
6.4,
6.3,
4.6,
6.5,
7.5,
7.5,
5.3,
7.5,
3.3,
3.5,
9.3,
4.8,
6.9,
6,
7.3,
6.6,
7.5,
6.9,
6.8,
6.3,
6.4,
5.6,
6.3,
7.3,
6.6,
4.6,
5.1,
5.6,
5.3,
5.6,
5.9,
4.7,
4.8,
6.8,
5.4,
5.1,
7,
4,
7.3,
6.8,
7,
7.1,
6.9,
7.3,
8.2,
7.1,
7.7,
6.5,
4.9,
6.4,
5.9,
6.2,
5.8,
6.7,
5.9,
7.3,
4.1,
4.9,
7.9,
5.6,
5.2,
4.1,
6.6,
2.9,
6.5,
7.2,
6.8,
7.8,
6.7,
7.1,
5.7,
5.3,
7.7,
6.1,
7.3,
7.2,
5.3,
6.1,
5.8,
5.7,
6.7,
6.5,
7.2,
7.6,
4.6,
6.9,
6.6,
6.3,
6.2,
5.3,
7.3,
5.6,
6.2,
5.2,
5.3,
5.4,
4.9,
5.5,
6.7,
3.9,
7.2,
5.1,
6.5,
8.2,
7.7,
7.2,
6.1,
8.8,
6.8,
6.8,
6.7,
7.1,
7.1,
6.1,
8,
7.5,
6.6,
5.4,
6.1,
6.1,
5.6,
5.8,
2.8,
6.7,
5.1,
7.2,
6,
6.7,
6.2,
6.2,
6.8,
7.1,
7.1,
7,
7.1,
6.4,
7,
6.2,
7.5,
4.8,
7.3,
5.8,
7.6,
5.6,
7,
6.6,
6.5,
7.4,
4.6,
6.4,
6,
5.9,
6.4,
6.6,
6.9,
6.9,
5.8,
6.4,
5.3,
6.5,
5.7,
6.7,
3.9,
4.1,
6.2,
3.8,
5.1,
7.8,
7.8,
6.1,
5.8,
6.3,
5.4,
7.3,
6.8,
7.3,
6.5,
7.2,
6.3,
5.9,
7.8,
7.4,
4.8,
6.3,
7.8,
7.5,
6.8,
6.6,
4.6,
7.1,
6.1,
6.7,
7.1,
5.8,
6.7,
5.8,
6.8,
8.5,
6.6,
7.7,
4.7,
6.4,
5.5,
8.6,
7,
7.1,
5.7,
3.7,
7.5,
4.6,
4.9,
6.9,
7.1,
5.8,
5.4,
7.3,
7.1,
5.8,
8.1,
5.7,
4.4,
7.9,
7.6,
4.8,
6.7,
2.7,
5.8,
7.5,
5.4,
4.1,
5.9,
6.3,
6.8,
2.3,
6.9,
8.1,
6.1,
5,
5.5,
6.2,
6.2,
6.3,
6.7,
3.5,
7.5,
6.6,
7.5,
7.2,
4.8,
6.6,
3.5,
7.6,
6.3,
5.5,
6.3,
6.5,
6.9,
7.6,
3.9,
6.1,
7.3,
8.3,
5.8,
6.8,
7,
5.9,
6.5,
6.4,
5.8,
5.1,
6.8,
5.3,
5.3,
4.9,
6.8,
7.1,
6.1,
8.5,
5.9,
6.3,
5.9,
5.4,
6.9,
7.5,
8.2,
5.9,
5,
7.3,
6.4,
6.6,
7.8,
4,
7.6,
7.7,
5.8,
5.2,
5.6,
5.3,
6.6,
1.9,
5.7,
6.6,
6,
6.1,
4.8,
6.2,
7.5,
6.3,
7.1,
6.6,
6.1,
6.7,
5.6,
7.2,
4.3,
6.4,
7.1,
6.3,
7.4,
6.1,
6.6,
6,
6.8,
6.8,
7.2,
1.9,
5.5,
4.5,
6.3,
6.7,
2.8,
5,
4.3,
5.6,
6.2,
5.3,
7.4,
7.4,
6.5,
7.1,
7.2,
2.3,
6.4,
6.1,
7,
7,
7,
4.9,
6.9,
7.5,
6.9,
4.5,
7.4,
7,
2.8,
7.5,
7.1,
6.4,
6.7,
5.3,
6.2,
6.4,
5.1,
5.5,
5.4,
7.5,
7.4,
8,
5.7,
6.8,
5.9,
7.2,
5.5,
8.5,
5.6,
4.1,
6.1,
5.4,
7.1,
3.6,
6.5,
8.6,
7,
7.6,
6.5,
6.4,
6.3,
5.7,
6.3,
6,
7.7,
6.2,
7.7,
6.4,
6.4,
6.9,
7.3,
7.3,
6.2,
6.6,
6.7,
5.7,
3.1,
6.3,
5.7,
7.1,
7,
6.1,
6.6,
7.8,
8.3,
3.9,
7,
6.7,
7.3,
6.3,
7.8,
7.3,
7.6,
5.3,
7.9,
5.3,
6.8,
7.1,
5.8,
5.8,
8.3,
5.6,
6.8,
5,
7.6,
6.7,
6.7,
5.7,
5.2,
7.5,
7.2,
5.3,
6.5,
5,
6.1,
4.4,
7.5,
5.7,
5.5,
7.1,
5.9,
6.7,
7,
7.9,
6.9,
7.3,
7.3,
3.5,
7.8,
6.7,
6.4,
7.1,
7.8,
5.9,
7.2,
6.2,
6.7,
7.6,
6.2,
6.5,
8.1,
6.3,
4.4,
6,
7.6,
8.4,
7.9,
5.6,
6.5,
7.5,
6.3,
7.9,
5.1,
6.7,
6.7,
5.6,
5.6,
6.8,
6.2,
5.6,
6.4,
5.6,
7.4,
7.2,
4.9,
7.5,
4.8,
3.1,
5.8,
6.7,
6.5,
5.9,
5.5,
3.6,
7.4,
3,
7.6,
6.4,
6.9,
6.6,
5.5,
4.1,
6.8,
6.5,
7.4,
7.7,
7.1,
6.3,
7.6,
8,
7.3,
7.6,
7.8,
6.5,
6.4,
8,
4.8,
7.8,
5.9,
5.4,
3.3,
8.2,
5.4,
6.4,
4.8,
5.9,
5.5,
7.9,
4.9,
7.2,
5.3,
7.2,
5.1,
5.6,
7.6,
7.2,
5.7,
5.2,
7.7,
7,
6,
6.6,
6.8,
7.2,
7.2,
2.8,
6.6,
6.7,
7,
4.4,
6.2,
7.3,
5.1,
6.6,
4.5,
5.9,
6.6,
6.5,
7.3,
7.5,
5.9,
7.4,
6.9,
7.9,
8.4,
8,
6,
6.8,
7.8,
8.1,
6.1,
6.2,
6.2,
7.4,
6.6,
7.3,
7.5,
5.6,
7.3,
6.4,
5,
5.4,
7.1,
5.3,
6.5,
6.2,
6.4,
6.9,
5.7,
7.7,
5.4,
5.6,
7.7,
5.1,
6.8,
8.4,
4.9,
7.1,
6.6,
6.1,
4.1,
5.8,
8.1,
7.6,
7.8,
4.6,
6,
7,
6.7,
6.4,
7.2,
7.4,
4.8,
4,
6.2,
7.7,
6.7,
7.9,
7.9,
5.5,
6.2,
5.1,
4.1,
6.7,
4.7,
6.4,
6.3,
5.5,
7.3,
6.3,
4.9,
7.6,
6,
6.2,
6.8,
4.5,
5.7,
4.6,
6.2,
7,
6.9,
6.7,
5.6,
6.6,
6.4,
2.8,
5.4,
5,
5.1,
8,
5.9,
8.2,
7,
6.6,
6.7,
5.5,
4.9,
6.9,
5.6,
8,
5.3,
6.2,
5.3,
6.6,
7.2,
4.6,
7.5,
6.5,
7.6,
6.2,
8,
6.3,
7.2,
6.7,
5.3,
6.3,
6.5,
8.3,
7.2,
6.8,
6.4,
6.9,
6.2,
6.1,
5.1,
4.5,
5.9,
8.1,
5.7,
6.8,
7.5,
8.3,
7.4,
8,
6.9,
6.9,
5.5,
7.2,
6.9,
5.5,
5.2,
7.1,
5.5,
6.7,
5,
6.4,
6.6,
5.9,
5.7,
4.5,
5,
4.6,
6.5,
4.9,
6,
6.9,
5.7,
6.9,
4.4,
7,
5.4,
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"## Budget\n",
"fig = px.histogram(df, x=\"budget\")\n",
"fig.update_traces(marker_color='blue', marker_line_color='blue', opacity=0.6)\n",
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"The relationship between movie gross and its budget is shown below. There is a moderate positive correlation between these two, which is fairly intuitive due to the similarities between the two histograms above. Essentially, as the movie budget increases, the movie's gross should increase too, the extent of which will be discussed in the correlation analysis section."
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"source": [
"## Actor 2 by IMDb score best\n",
"a5 = df.groupby('actor_2_name')['imdb_score'].mean().sort_values(ascending=False).head(10)\n",
"a5 = pd.DataFrame(data=a5).reset_index()\n",
"a5.columns= ['actor_2_name', 'avg_imdb_score']\n",
"fig = px.bar(a5, x=\"actor_2_name\", y='avg_imdb_score')\n",
"fig.update_traces(marker_color='blue', marker_line_color='blue', opacity=0.6)\n",
"fig.update_layout(title_text='Top 10 Best Supporting Actors')"
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"source": [
"### d. Facebook"
]
},
{
"cell_type": "markdown",
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"source": [
"The scatter plot below shows the relationship between movie Facebook likes and IMDb score. There's a substantial amount of Facebook likes in the dataset that are 0. This means that a lot of these movies just don't have Facebook pages for one reason or another. Because of this, the variable may not be a strong determinant of IMDb score, but we'll discuss this more in the correlation analysis section."
]
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6.4,
5.7,
6.1,
5.4,
6.7,
6.8,
6,
7.8,
5.3,
4.5,
5.4,
7.8,
7.2,
6.6,
7.6,
5.9,
6.7,
7.7,
5.4,
6.9,
7.7,
6.8,
6.4,
5.7,
7.3,
6.8,
6.3,
5.9,
7.4,
8.3,
6.2,
6.3,
5.8,
7.5,
6.3,
6.4,
7.2,
6.3,
6.9,
6.6,
6,
7.5,
7.7,
6.2,
5.4,
6.6,
5.3,
5.6,
5.9,
7.8,
6.7,
7.4,
6.2,
5.4,
6.7,
5.3,
5.9,
4.8,
3.8,
8.5,
6.8,
5.3,
7.3,
6.6,
6.2,
5.2,
6.2,
6.2,
6.6,
6.2,
5.1,
6.6,
6.1,
6.6,
5.9,
6.3,
7.1,
5,
5.6,
7.4,
4.5,
6.2,
5,
6.5,
5.1,
6.5,
6.2,
6.3,
3.8,
6.2,
5.7,
6.7,
6.8,
6,
7.3,
5.5,
6.7,
4.8,
5.7,
5.1,
6,
4.2,
7.4,
4.6,
6.9,
6.9,
8,
6.4,
6.3,
6.8,
6.8,
5.4,
7.2,
7.3,
5.2,
5.5,
7.7,
7.1,
5.3,
5.6,
5.7,
7.1,
7.6,
5.5,
5.1,
4.9,
6.5,
5.6,
5.3,
6.5,
6.8,
6.5,
6,
8.4,
6,
7.6,
6.9,
6.4,
5.1,
7,
5.7,
6.8,
6.7,
6.2,
7.2,
6.2,
5.6,
4.4,
7.5,
7.1,
6.4,
7.1,
6.9,
7.5,
6.3,
6.4,
5.9,
6.8,
6.3,
3.6,
5.3,
5.9,
6.9,
6.9,
6.1,
8.5,
6.3,
7.3,
6.3,
7.2,
7.3,
6.3,
8.1,
6.9,
6.3,
7.3,
5.5,
6.1,
6.9,
7.2,
6.4,
6.4,
8.3,
7.2,
6.8,
6.5,
7.8,
7.6,
7.2,
6.7,
6.8,
6.3,
6.2,
6.2,
8.6,
8,
7,
8,
8.1,
6.7,
7.9,
6.1,
4.2,
6.1,
6.6,
7.5,
7.4,
7.2,
6.9,
7.4,
5.4,
6.8,
6.3,
7.2,
6.9,
6,
5.9,
5.4,
5.9,
6.1,
7.7,
5.8,
7.6,
6.1,
5.4,
5.1,
6.4,
6.3,
7.5,
7.1,
7.8,
6.5,
6.6,
7.4,
7.6,
7.5,
6.6,
7.2,
7.6,
6.2,
5.6,
7.6,
6.6,
7,
2.7,
7.6,
6.6,
6.9,
6.8,
3.7,
6.1,
5.9,
6.7,
6.9,
5.5,
7.1,
7.1,
7.3,
3.4,
6.8,
6.9,
7,
5.5,
5.1,
6.2,
5.9,
5.2,
6.2,
5.5,
7.4,
4.4,
6.3,
6.1,
5.3,
5.4,
6.7,
5.9,
7.3,
5.5,
5.8,
4.6,
6.7,
5.1,
5.6,
7,
6.4,
6.7,
4.1,
5.5,
2.7,
6.4,
4.8,
6.1,
4.8,
7,
6.8,
5.6,
6.1,
7.9,
8.4,
6.5,
7.1,
6.6,
7,
5.6,
4.8,
7.5,
6,
6.8,
6.5,
7.9,
6.4,
5.8,
7.7,
5.3,
5.3,
7.5,
6.9,
4.9,
7.1,
8,
7.9,
7.6,
5.9,
6.3,
6.4,
8.2,
6.9,
7.8,
6.7,
7.5,
7.4,
5.2,
7.6,
7.3,
6.6,
6.8,
6.9,
5.8,
6.6,
6.7,
6.7,
6.3,
7.7,
6.1,
4.9,
6.2,
7.8,
8.2,
6.9,
6.2,
6.9,
4.8,
8,
5.3,
6.7,
5.4,
5.4,
4.9,
6.1,
5.8,
7,
6.5,
6.6,
5.7,
6.6,
7,
7.4,
5.3,
7.4,
7.4,
6.8,
7.2,
6,
7.8,
6.6,
7.9,
5.7,
7.1,
5.6,
7.8,
7.9,
6.9,
7.7,
6.9,
6,
6.2,
5.9,
6.8,
3.6,
6.7,
6.3,
6.4,
6.4,
5.7,
6.2,
5.2,
6.1,
7.1,
7.2,
6.5,
6,
7,
7,
7.5,
6.6,
7.4,
6.5,
6.2,
7.8,
5.2,
6.5,
6.5,
5.2,
7.2,
7.1,
4.5,
5.7,
6,
6.4,
5.2,
4.3,
6.1,
6.8,
5.2,
6.5,
7.5,
7.1,
6.9,
8,
8.2,
6.4,
7.9,
6.7,
6.1,
8.9,
8.1,
6.2,
4.9,
5.8,
6,
7,
6,
7.9,
8.1,
6.2,
6.7,
7.3,
4.6,
6.1,
6.2,
7.8,
6.1,
5.8,
6.5,
7.2,
7.8,
4.7,
6.8,
5.9,
7.2,
8.7,
5,
6.6,
8.3,
6.7,
7.8,
6.5,
6.1,
8.1,
5.2,
5.6,
5.8,
6.6,
6.6,
5.5,
7,
6.5,
5.8,
5.6,
5.6,
5.8,
7.6,
6.4,
6.3,
4.6,
6.5,
7.5,
7.5,
5.3,
7.5,
3.3,
3.5,
9.3,
4.8,
6.9,
6,
7.3,
6.6,
7.5,
6.9,
6.8,
6.3,
6.4,
5.6,
6.3,
7.3,
6.6,
4.6,
5.1,
5.6,
5.3,
5.6,
5.9,
4.7,
4.8,
6.8,
5.4,
5.1,
7,
4,
7.3,
6.8,
7,
7.1,
6.9,
7.3,
8.2,
7.1,
7.7,
6.5,
4.9,
6.4,
5.9,
6.2,
5.8,
6.7,
5.9,
7.3,
4.1,
4.9,
7.9,
5.6,
5.2,
4.1,
6.6,
2.9,
6.5,
7.2,
6.8,
7.8,
6.7,
7.1,
5.7,
5.3,
7.7,
6.1,
7.3,
7.2,
5.3,
6.1,
5.8,
5.7,
6.7,
6.5,
7.2,
7.6,
4.6,
6.9,
6.6,
6.3,
6.2,
5.3,
7.3,
5.6,
6.2,
5.2,
5.3,
5.4,
4.9,
5.5,
6.7,
3.9,
7.2,
5.1,
6.5,
8.2,
7.7,
7.2,
6.1,
8.8,
6.8,
6.8,
6.7,
7.1,
7.1,
6.1,
8,
7.5,
6.6,
5.4,
6.1,
6.1,
5.6,
5.8,
2.8,
6.7,
5.1,
7.2,
6,
6.7,
6.2,
6.2,
6.8,
7.1,
7.1,
7,
7.1,
6.4,
7,
6.2,
7.5,
4.8,
7.3,
5.8,
7.6,
5.6,
7,
6.6,
6.5,
7.4,
4.6,
6.4,
6,
5.9,
6.4,
6.6,
6.9,
6.9,
5.8,
6.4,
5.3,
6.5,
5.7,
6.7,
3.9,
4.1,
6.2,
3.8,
5.1,
7.8,
7.8,
6.1,
5.8,
6.3,
5.4,
7.3,
6.8,
7.3,
6.5,
7.2,
6.3,
5.9,
7.8,
7.4,
4.8,
6.3,
7.8,
7.5,
6.8,
6.6,
4.6,
7.1,
6.1,
6.7,
7.1,
5.8,
6.7,
5.8,
6.8,
8.5,
6.6,
7.7,
4.7,
6.4,
5.5,
8.6,
7,
7.1,
5.7,
3.7,
7.5,
4.6,
4.9,
6.9,
7.1,
5.8,
5.4,
7.3,
7.1,
5.8,
8.1,
5.7,
4.4,
7.9,
7.6,
4.8,
6.7,
2.7,
5.8,
7.5,
5.4,
4.1,
5.9,
6.3,
6.8,
2.3,
6.9,
8.1,
6.1,
5,
5.5,
6.2,
6.2,
6.3,
6.7,
3.5,
7.5,
6.6,
7.5,
7.2,
4.8,
6.6,
3.5,
7.6,
6.3,
5.5,
6.3,
6.5,
6.9,
7.6,
3.9,
6.1,
7.3,
8.3,
5.8,
6.8,
7,
5.9,
6.5,
6.4,
5.8,
5.1,
6.8,
5.3,
5.3,
4.9,
6.8,
7.1,
6.1,
8.5,
5.9,
6.3,
5.9,
5.4,
6.9,
7.5,
8.2,
5.9,
5,
7.3,
6.4,
6.6,
7.8,
4,
7.6,
7.7,
5.8,
5.2,
5.6,
5.3,
6.6,
1.9,
5.7,
6.6,
6,
6.1,
4.8,
6.2,
7.5,
6.3,
7.1,
6.6,
6.1,
6.7,
5.6,
7.2,
4.3,
6.4,
7.1,
6.3,
7.4,
6.1,
6.6,
6,
6.8,
6.8,
7.2,
1.9,
5.5,
4.5,
6.3,
6.7,
2.8,
5,
4.3,
5.6,
6.2,
5.3,
7.4,
7.4,
6.5,
7.1,
7.2,
2.3,
6.4,
6.1,
7,
7,
7,
4.9,
6.9,
7.5,
6.9,
4.5,
7.4,
7,
2.8,
7.5,
7.1,
6.4,
6.7,
5.3,
6.2,
6.4,
5.1,
5.5,
5.4,
7.5,
7.4,
8,
5.7,
6.8,
5.9,
7.2,
5.5,
8.5,
5.6,
4.1,
6.1,
5.4,
7.1,
3.6,
6.5,
8.6,
7,
7.6,
6.5,
6.4,
6.3,
5.7,
6.3,
6,
7.7,
6.2,
7.7,
6.4,
6.4,
6.9,
7.3,
7.3,
6.2,
6.6,
6.7,
5.7,
3.1,
6.3,
5.7,
7.1,
7,
6.1,
6.6,
7.8,
8.3,
3.9,
7,
6.7,
7.3,
6.3,
7.8,
7.3,
7.6,
5.3,
7.9,
5.3,
6.8,
7.1,
5.8,
5.8,
8.3,
5.6,
6.8,
5,
7.6,
6.7,
6.7,
5.7,
5.2,
7.5,
7.2,
5.3,
6.5,
5,
6.1,
4.4,
7.5,
5.7,
5.5,
7.1,
5.9,
6.7,
7,
7.9,
6.9,
7.3,
7.3,
3.5,
7.8,
6.7,
6.4,
7.1,
7.8,
5.9,
7.2,
6.2,
6.7,
7.6,
6.2,
6.5,
8.1,
6.3,
4.4,
6,
7.6,
8.4,
7.9,
5.6,
6.5,
7.5,
6.3,
7.9,
5.1,
6.7,
6.7,
5.6,
5.6,
6.8,
6.2,
5.6,
6.4,
5.6,
7.4,
7.2,
4.9,
7.5,
4.8,
3.1,
5.8,
6.7,
6.5,
5.9,
5.5,
3.6,
7.4,
3,
7.6,
6.4,
6.9,
6.6,
5.5,
4.1,
6.8,
6.5,
7.4,
7.7,
7.1,
6.3,
7.6,
8,
7.3,
7.6,
7.8,
6.5,
6.4,
8,
4.8,
7.8,
5.9,
5.4,
3.3,
8.2,
5.4,
6.4,
4.8,
5.9,
5.5,
7.9,
4.9,
7.2,
5.3,
7.2,
5.1,
5.6,
7.6,
7.2,
5.7,
5.2,
7.7,
7,
6,
6.6,
6.8,
7.2,
7.2,
2.8,
6.6,
6.7,
7,
4.4,
6.2,
7.3,
5.1,
6.6,
4.5,
5.9,
6.6,
6.5,
7.3,
7.5,
5.9,
7.4,
6.9,
7.9,
8.4,
8,
6,
6.8,
7.8,
8.1,
6.1,
6.2,
6.2,
7.4,
6.6,
7.3,
7.5,
5.6,
7.3,
6.4,
5,
5.4,
7.1,
5.3,
6.5,
6.2,
6.4,
6.9,
5.7,
7.7,
5.4,
5.6,
7.7,
5.1,
6.8,
8.4,
4.9,
7.1,
6.6,
6.1,
4.1,
5.8,
8.1,
7.6,
7.8,
4.6,
6,
7,
6.7,
6.4,
7.2,
7.4,
4.8,
4,
6.2,
7.7,
6.7,
7.9,
7.9,
5.5,
6.2,
5.1,
4.1,
6.7,
4.7,
6.4,
6.3,
5.5,
7.3,
6.3,
4.9,
7.6,
6,
6.2,
6.8,
4.5,
5.7,
4.6,
6.2,
7,
6.9,
6.7,
5.6,
6.6,
6.4,
2.8,
5.4,
5,
5.1,
8,
5.9,
8.2,
7,
6.6,
6.7,
5.5,
4.9,
6.9,
5.6,
8,
5.3,
6.2,
5.3,
6.6,
7.2,
4.6,
7.5,
6.5,
7.6,
6.2,
8,
6.3,
7.2,
6.7,
5.3,
6.3,
6.5,
8.3,
7.2,
6.8,
6.4,
6.9,
6.2,
6.1,
5.1,
4.5,
5.9,
8.1,
5.7,
6.8,
7.5,
8.3,
7.4,
8,
6.9,
6.9,
5.5,
7.2,
6.9,
5.5,
5.2,
7.1,
5.5,
6.7,
5,
6.4,
6.6,
5.9,
5.7,
4.5,
5,
4.6,
6.5,
4.9,
6,
6.9,
5.7,
6.9,
4.4,
7,
5.4,
5.4,
7.6,
5.9,
6.6,
6.7,
3.9,
5.7,
6.5,
6.8,
7.3,
7,
6.5,
7.7,
7.7,
5.9,
6.8,
7.4,
5.1,
7.4,
7.2,
8.3,
8.1,
7.3,
3.6,
1.6,
8,
6.2,
9,
6.1,
5.7,
6.8,
5.5,
6.8,
7.3,
6.1,
7.2,
5.9,
6.1,
6.8,
7.7,
4.9,
6.1,
2.5,
6.1,
5.9,
5.7,
5.6,
7.2,
7.7,
7.8,
6.1,
5.8,
6.5,
7.9,
6.3,
3.8,
8.3,
6.4,
6.7,
6.1,
6,
5.8,
5.6,
6.1,
5.9,
7.3,
6.8,
5.7,
7.3,
6.3,
5.9,
7.1,
7.1,
8,
5.1,
7.1,
6.5,
4.5,
6.6,
4.3,
6.7,
5.4,
6.6,
7.3,
6.9,
8,
7.8,
6.1,
5.1,
7.4,
7.8,
8,
6.7,
6.6,
6.4,
6.7,
6.2,
7.3,
8.1,
7,
8,
8,
7,
7.9,
5.9,
6.6,
6.3,
7.7,
6.9,
7.1,
7.4,
6.5,
6.5,
6.8,
7.5,
6.6,
7.1,
6.6,
7,
3.3,
6.7,
6.8,
6,
5.4,
4.3,
6.2,
7.7,
8,
7.4,
5.9,
7.8,
7.4,
6.5,
7,
7.6,
6.9,
5.3,
6.4,
7.8,
6.7,
5.3,
6.3,
7,
6.6,
8.4,
5.4,
7.8,
7.6,
6.6,
6.4,
7,
5.7,
5.9,
6.3,
6.3,
6.2,
2.1,
5,
5.3,
7.1,
7,
7.1,
7,
7.7,
7.1,
6.8,
7.5,
6.3,
7.3,
6.8,
7.2,
6.4,
6,
6.4,
7.5,
4.6,
7.7,
6.7,
5.6,
7.5,
5.8,
8.3,
6.6,
7.2,
8.7,
6,
8,
4.5,
7.9,
7.5,
6.8,
7.2,
7.1,
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"source": [
"## Movie likes by IMDb score\n",
"fig = px.scatter(df, x=\"movie_facebook_likes\", y=\"imdb_score\", hover_name='movie_title')\n",
"fig.update_traces(marker_color='blue', marker_line_color='blue', opacity=0.6)\n",
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"The director likes by IMDb score scatter plot tells a similar story to that of movie Facebook likes. At the time of data collection, many of the directors do not have a Facebook page."
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"10 Things I Hate About You ",
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"The Butterfly Effect ",
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"[Rec] 2 ",
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"Instructions Not Included ",
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"You Can Count on Me ",
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3.3,
5.9,
5.8,
4.7,
4.1,
6.8,
6.2,
4.5,
5.8,
7.3,
5.9,
4.4,
5.8,
5.1,
6.9,
6.2,
6.9,
7.3,
7.1,
6,
7,
7.6,
7.1,
6.7,
7,
8,
5.3,
4.9,
6.4,
6.1,
6.5,
5.7,
5.1,
6.6,
6.5,
6.9,
7.6,
5.6,
6.2,
4.4,
5.6,
5.5,
6.7,
6.1,
6.2,
7.3,
6.6,
8.2,
6.4,
6.4,
5.2,
6.5,
7.1,
7.3,
5.2,
7.7,
7.6,
5.7,
7,
6,
8.1,
8,
5.6,
6.1,
6.9,
5.2,
7,
6.3,
7,
6.9,
6.2,
6.4,
6.4,
5.7,
6.1,
5.4,
6.7,
6.8,
6,
7.8,
5.3,
4.5,
5.4,
7.8,
7.2,
6.6,
7.6,
5.9,
6.7,
7.7,
5.4,
6.9,
7.7,
6.8,
6.4,
5.7,
7.3,
6.8,
6.3,
5.9,
7.4,
8.3,
6.2,
6.3,
5.8,
7.5,
6.3,
6.4,
7.2,
6.3,
6.9,
6.6,
6,
7.5,
7.7,
6.2,
5.4,
6.6,
5.3,
5.6,
5.9,
7.8,
6.7,
7.4,
6.2,
5.4,
6.7,
5.3,
5.9,
4.8,
3.8,
8.5,
6.8,
5.3,
7.3,
6.6,
6.2,
5.2,
6.2,
6.2,
6.6,
6.2,
5.1,
6.6,
6.1,
6.6,
5.9,
6.3,
7.1,
5,
5.6,
7.4,
4.5,
6.2,
5,
6.5,
5.1,
6.5,
6.2,
6.3,
3.8,
6.2,
5.7,
6.7,
6.8,
6,
7.3,
5.5,
6.7,
4.8,
5.7,
5.1,
6,
4.2,
7.4,
4.6,
6.9,
6.9,
8,
6.4,
6.3,
6.8,
6.8,
5.4,
7.2,
7.3,
5.2,
5.5,
7.7,
7.1,
5.3,
5.6,
5.7,
7.1,
7.6,
5.5,
5.1,
4.9,
6.5,
5.6,
5.3,
6.5,
6.8,
6.5,
6,
8.4,
6,
7.6,
6.9,
6.4,
5.1,
7,
5.7,
6.8,
6.7,
6.2,
7.2,
6.2,
5.6,
4.4,
7.5,
7.1,
6.4,
7.1,
6.9,
7.5,
6.3,
6.4,
5.9,
6.8,
6.3,
3.6,
5.3,
5.9,
6.9,
6.9,
6.1,
8.5,
6.3,
7.3,
6.3,
7.2,
7.3,
6.3,
8.1,
6.9,
6.3,
7.3,
5.5,
6.1,
6.9,
7.2,
6.4,
6.4,
8.3,
7.2,
6.8,
6.5,
7.8,
7.6,
7.2,
6.7,
6.8,
6.3,
6.2,
6.2,
8.6,
8,
7,
8,
8.1,
6.7,
7.9,
6.1,
4.2,
6.1,
6.6,
7.5,
7.4,
7.2,
6.9,
7.4,
5.4,
6.8,
6.3,
7.2,
6.9,
6,
5.9,
5.4,
5.9,
6.1,
7.7,
5.8,
7.6,
6.1,
5.4,
5.1,
6.4,
6.3,
7.5,
7.1,
7.8,
6.5,
6.6,
7.4,
7.6,
7.5,
6.6,
7.2,
7.6,
6.2,
5.6,
7.6,
6.6,
7,
2.7,
7.6,
6.6,
6.9,
6.8,
3.7,
6.1,
5.9,
6.7,
6.9,
5.5,
7.1,
7.1,
7.3,
3.4,
6.8,
6.9,
7,
5.5,
5.1,
6.2,
5.9,
5.2,
6.2,
5.5,
7.4,
4.4,
6.3,
6.1,
5.3,
5.4,
6.7,
5.9,
7.3,
5.5,
5.8,
4.6,
6.7,
5.1,
5.6,
7,
6.4,
6.7,
4.1,
5.5,
2.7,
6.4,
4.8,
6.1,
4.8,
7,
6.8,
5.6,
6.1,
7.9,
8.4,
6.5,
7.1,
6.6,
7,
5.6,
4.8,
7.5,
6,
6.8,
6.5,
7.9,
6.4,
5.8,
7.7,
5.3,
5.3,
7.5,
6.9,
4.9,
7.1,
8,
7.9,
7.6,
5.9,
6.3,
6.4,
8.2,
6.9,
7.8,
6.7,
7.5,
7.4,
5.2,
7.6,
7.3,
6.6,
6.8,
6.9,
5.8,
6.6,
6.7,
6.7,
6.3,
7.7,
6.1,
4.9,
6.2,
7.8,
8.2,
6.9,
6.2,
6.9,
4.8,
8,
5.3,
6.7,
5.4,
5.4,
4.9,
6.1,
5.8,
7,
6.5,
6.6,
5.7,
6.6,
7,
7.4,
5.3,
7.4,
7.4,
6.8,
7.2,
6,
7.8,
6.6,
7.9,
5.7,
7.1,
5.6,
7.8,
7.9,
6.9,
7.7,
6.9,
6,
6.2,
5.9,
6.8,
3.6,
6.7,
6.3,
6.4,
6.4,
5.7,
6.2,
5.2,
6.1,
7.1,
7.2,
6.5,
6,
7,
7,
7.5,
6.6,
7.4,
6.5,
6.2,
7.8,
5.2,
6.5,
6.5,
5.2,
7.2,
7.1,
4.5,
5.7,
6,
6.4,
5.2,
4.3,
6.1,
6.8,
5.2,
6.5,
7.5,
7.1,
6.9,
8,
8.2,
6.4,
7.9,
6.7,
6.1,
8.9,
8.1,
6.2,
4.9,
5.8,
6,
7,
6,
7.9,
8.1,
6.2,
6.7,
7.3,
4.6,
6.1,
6.2,
7.8,
6.1,
5.8,
6.5,
7.2,
7.8,
4.7,
6.8,
5.9,
7.2,
8.7,
5,
6.6,
8.3,
6.7,
7.8,
6.5,
6.1,
8.1,
5.2,
5.6,
5.8,
6.6,
6.6,
5.5,
7,
6.5,
5.8,
5.6,
5.6,
5.8,
7.6,
6.4,
6.3,
4.6,
6.5,
7.5,
7.5,
5.3,
7.5,
3.3,
3.5,
9.3,
4.8,
6.9,
6,
7.3,
6.6,
7.5,
6.9,
6.8,
6.3,
6.4,
5.6,
6.3,
7.3,
6.6,
4.6,
5.1,
5.6,
5.3,
5.6,
5.9,
4.7,
4.8,
6.8,
5.4,
5.1,
7,
4,
7.3,
6.8,
7,
7.1,
6.9,
7.3,
8.2,
7.1,
7.7,
6.5,
4.9,
6.4,
5.9,
6.2,
5.8,
6.7,
5.9,
7.3,
4.1,
4.9,
7.9,
5.6,
5.2,
4.1,
6.6,
2.9,
6.5,
7.2,
6.8,
7.8,
6.7,
7.1,
5.7,
5.3,
7.7,
6.1,
7.3,
7.2,
5.3,
6.1,
5.8,
5.7,
6.7,
6.5,
7.2,
7.6,
4.6,
6.9,
6.6,
6.3,
6.2,
5.3,
7.3,
5.6,
6.2,
5.2,
5.3,
5.4,
4.9,
5.5,
6.7,
3.9,
7.2,
5.1,
6.5,
8.2,
7.7,
7.2,
6.1,
8.8,
6.8,
6.8,
6.7,
7.1,
7.1,
6.1,
8,
7.5,
6.6,
5.4,
6.1,
6.1,
5.6,
5.8,
2.8,
6.7,
5.1,
7.2,
6,
6.7,
6.2,
6.2,
6.8,
7.1,
7.1,
7,
7.1,
6.4,
7,
6.2,
7.5,
4.8,
7.3,
5.8,
7.6,
5.6,
7,
6.6,
6.5,
7.4,
4.6,
6.4,
6,
5.9,
6.4,
6.6,
6.9,
6.9,
5.8,
6.4,
5.3,
6.5,
5.7,
6.7,
3.9,
4.1,
6.2,
3.8,
5.1,
7.8,
7.8,
6.1,
5.8,
6.3,
5.4,
7.3,
6.8,
7.3,
6.5,
7.2,
6.3,
5.9,
7.8,
7.4,
4.8,
6.3,
7.8,
7.5,
6.8,
6.6,
4.6,
7.1,
6.1,
6.7,
7.1,
5.8,
6.7,
5.8,
6.8,
8.5,
6.6,
7.7,
4.7,
6.4,
5.5,
8.6,
7,
7.1,
5.7,
3.7,
7.5,
4.6,
4.9,
6.9,
7.1,
5.8,
5.4,
7.3,
7.1,
5.8,
8.1,
5.7,
4.4,
7.9,
7.6,
4.8,
6.7,
2.7,
5.8,
7.5,
5.4,
4.1,
5.9,
6.3,
6.8,
2.3,
6.9,
8.1,
6.1,
5,
5.5,
6.2,
6.2,
6.3,
6.7,
3.5,
7.5,
6.6,
7.5,
7.2,
4.8,
6.6,
3.5,
7.6,
6.3,
5.5,
6.3,
6.5,
6.9,
7.6,
3.9,
6.1,
7.3,
8.3,
5.8,
6.8,
7,
5.9,
6.5,
6.4,
5.8,
5.1,
6.8,
5.3,
5.3,
4.9,
6.8,
7.1,
6.1,
8.5,
5.9,
6.3,
5.9,
5.4,
6.9,
7.5,
8.2,
5.9,
5,
7.3,
6.4,
6.6,
7.8,
4,
7.6,
7.7,
5.8,
5.2,
5.6,
5.3,
6.6,
1.9,
5.7,
6.6,
6,
6.1,
4.8,
6.2,
7.5,
6.3,
7.1,
6.6,
6.1,
6.7,
5.6,
7.2,
4.3,
6.4,
7.1,
6.3,
7.4,
6.1,
6.6,
6,
6.8,
6.8,
7.2,
1.9,
5.5,
4.5,
6.3,
6.7,
2.8,
5,
4.3,
5.6,
6.2,
5.3,
7.4,
7.4,
6.5,
7.1,
7.2,
2.3,
6.4,
6.1,
7,
7,
7,
4.9,
6.9,
7.5,
6.9,
4.5,
7.4,
7,
2.8,
7.5,
7.1,
6.4,
6.7,
5.3,
6.2,
6.4,
5.1,
5.5,
5.4,
7.5,
7.4,
8,
5.7,
6.8,
5.9,
7.2,
5.5,
8.5,
5.6,
4.1,
6.1,
5.4,
7.1,
3.6,
6.5,
8.6,
7,
7.6,
6.5,
6.4,
6.3,
5.7,
6.3,
6,
7.7,
6.2,
7.7,
6.4,
6.4,
6.9,
7.3,
7.3,
6.2,
6.6,
6.7,
5.7,
3.1,
6.3,
5.7,
7.1,
7,
6.1,
6.6,
7.8,
8.3,
3.9,
7,
6.7,
7.3,
6.3,
7.8,
7.3,
7.6,
5.3,
7.9,
5.3,
6.8,
7.1,
5.8,
5.8,
8.3,
5.6,
6.8,
5,
7.6,
6.7,
6.7,
5.7,
5.2,
7.5,
7.2,
5.3,
6.5,
5,
6.1,
4.4,
7.5,
5.7,
5.5,
7.1,
5.9,
6.7,
7,
7.9,
6.9,
7.3,
7.3,
3.5,
7.8,
6.7,
6.4,
7.1,
7.8,
5.9,
7.2,
6.2,
6.7,
7.6,
6.2,
6.5,
8.1,
6.3,
4.4,
6,
7.6,
8.4,
7.9,
5.6,
6.5,
7.5,
6.3,
7.9,
5.1,
6.7,
6.7,
5.6,
5.6,
6.8,
6.2,
5.6,
6.4,
5.6,
7.4,
7.2,
4.9,
7.5,
4.8,
3.1,
5.8,
6.7,
6.5,
5.9,
5.5,
3.6,
7.4,
3,
7.6,
6.4,
6.9,
6.6,
5.5,
4.1,
6.8,
6.5,
7.4,
7.7,
7.1,
6.3,
7.6,
8,
7.3,
7.6,
7.8,
6.5,
6.4,
8,
4.8,
7.8,
5.9,
5.4,
3.3,
8.2,
5.4,
6.4,
4.8,
5.9,
5.5,
7.9,
4.9,
7.2,
5.3,
7.2,
5.1,
5.6,
7.6,
7.2,
5.7,
5.2,
7.7,
7,
6,
6.6,
6.8,
7.2,
7.2,
2.8,
6.6,
6.7,
7,
4.4,
6.2,
7.3,
5.1,
6.6,
4.5,
5.9,
6.6,
6.5,
7.3,
7.5,
5.9,
7.4,
6.9,
7.9,
8.4,
8,
6,
6.8,
7.8,
8.1,
6.1,
6.2,
6.2,
7.4,
6.6,
7.3,
7.5,
5.6,
7.3,
6.4,
5,
5.4,
7.1,
5.3,
6.5,
6.2,
6.4,
6.9,
5.7,
7.7,
5.4,
5.6,
7.7,
5.1,
6.8,
8.4,
4.9,
7.1,
6.6,
6.1,
4.1,
5.8,
8.1,
7.6,
7.8,
4.6,
6,
7,
6.7,
6.4,
7.2,
7.4,
4.8,
4,
6.2,
7.7,
6.7,
7.9,
7.9,
5.5,
6.2,
5.1,
4.1,
6.7,
4.7,
6.4,
6.3,
5.5,
7.3,
6.3,
4.9,
7.6,
6,
6.2,
6.8,
4.5,
5.7,
4.6,
6.2,
7,
6.9,
6.7,
5.6,
6.6,
6.4,
2.8,
5.4,
5,
5.1,
8,
5.9,
8.2,
7,
6.6,
6.7,
5.5,
4.9,
6.9,
5.6,
8,
5.3,
6.2,
5.3,
6.6,
7.2,
4.6,
7.5,
6.5,
7.6,
6.2,
8,
6.3,
7.2,
6.7,
5.3,
6.3,
6.5,
8.3,
7.2,
6.8,
6.4,
6.9,
6.2,
6.1,
5.1,
4.5,
5.9,
8.1,
5.7,
6.8,
7.5,
8.3,
7.4,
8,
6.9,
6.9,
5.5,
7.2,
6.9,
5.5,
5.2,
7.1,
5.5,
6.7,
5,
6.4,
6.6,
5.9,
5.7,
4.5,
5,
4.6,
6.5,
4.9,
6,
6.9,
5.7,
6.9,
4.4,
7,
5.4,
5.4,
7.6,
5.9,
6.6,
6.7,
3.9,
5.7,
6.5,
6.8,
7.3,
7,
6.5,
7.7,
7.7,
5.9,
6.8,
7.4,
5.1,
7.4,
7.2,
8.3,
8.1,
7.3,
3.6,
1.6,
8,
6.2,
9,
6.1,
5.7,
6.8,
5.5,
6.8,
7.3,
6.1,
7.2,
5.9,
6.1,
6.8,
7.7,
4.9,
6.1,
2.5,
6.1,
5.9,
5.7,
5.6,
7.2,
7.7,
7.8,
6.1,
5.8,
6.5,
7.9,
6.3,
3.8,
8.3,
6.4,
6.7,
6.1,
6,
5.8,
5.6,
6.1,
5.9,
7.3,
6.8,
5.7,
7.3,
6.3,
5.9,
7.1,
7.1,
8,
5.1,
7.1,
6.5,
4.5,
6.6,
4.3,
6.7,
5.4,
6.6,
7.3,
6.9,
8,
7.8,
6.1,
5.1,
7.4,
7.8,
8,
6.7,
6.6,
6.4,
6.7,
6.2,
7.3,
8.1,
7,
8,
8,
7,
7.9,
5.9,
6.6,
6.3,
7.7,
6.9,
7.1,
7.4,
6.5,
6.5,
6.8,
7.5,
6.6,
7.1,
6.6,
7,
3.3,
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"## Director likes by IMDb score\n",
"fig = px.scatter(df, x=\"director_facebook_likes\", y=\"imdb_score\", hover_name='movie_title')\n",
"fig.update_traces(marker_color='blue', marker_line_color='blue', opacity=0.6)\n",
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2179,
41293,
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1891,
2673,
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1986,
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239,
3421,
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1980,
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1739,
163,
862,
4590,
5963,
2306,
3282,
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16,
174,
170,
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1941,
155,
214,
1456,
99,
1062,
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768,
563,
218,
927,
2300,
29,
3163,
65,
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1958,
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2658,
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3970,
2423,
2095,
763,
822,
2721,
2196,
231,
137,
1694,
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10438,
1981,
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1173,
2990,
4762,
3030,
856,
859,
2667,
1582,
33441,
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2179,
352,
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1805,
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687,
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423,
663,
1983,
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116,
587,
14954,
3071,
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1986,
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3010,
132,
460,
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1987,
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5.5,
7.4,
8.7,
7.6,
5.5,
7.6,
6.5,
6.9,
6.7,
6.6,
7.2,
6.4,
6.4,
6,
6.1,
6,
6.4,
6.4,
7.3,
5.2,
6.6,
6.3,
5.9,
6.7,
5.4,
6.4,
6.7,
6.2,
6.1,
8.8,
7.1,
5.7,
5,
5.1,
6.9,
4.8,
6.5,
5.1,
7.1,
7.5,
6.2,
6.3,
8.1,
6.6,
6.9,
6.1,
4.3,
6.6,
6.8,
3.8,
5.9,
7.9,
6.3,
5.5,
7.7,
6.3,
7.1,
8.5,
5.8,
8.1,
7.9,
7.2,
6.3,
8.1,
7,
5.5,
6.7,
5.2,
7,
6.1,
6.6,
5.5,
5.9,
5.4,
6.4,
5.7,
6.7,
7.1,
6.8,
6.5,
7.6,
5.5,
6.5,
7,
5.8,
7.3,
6.6,
4.4,
7.7,
5,
7.7,
4.4,
6.1,
5.4,
6.8,
6.5,
7,
6.3,
6.3,
6.1,
6.1,
5.3,
5.4,
6.2,
6.6,
5.9,
6.3,
7.2,
6.8,
6.1,
7.8,
5,
6.2,
6.7,
4.9,
7.4,
6.2,
4.9,
6.1,
6.1,
6.4,
6.3,
6.6,
5.7,
5.9,
6,
6.1,
6.7,
6.7,
7.9,
4.3,
5.7,
6.7,
6.7,
6.1,
5.6,
6.6,
6.9,
4.8,
6.2,
6,
4.9,
5.6,
6.1,
6.1,
4.8,
5.5,
3.8,
6.5,
6.7,
8.1,
4.9,
7.3,
6.4,
6.7,
3.6,
5.7,
6,
4.7,
6.3,
5.9,
5.9,
7.5,
5.6,
6.4,
6.3,
4.3,
5.9,
5.5,
6.2,
8.8,
5.2,
7,
6.6,
7.3,
5.6,
6.6,
5.4,
6.3,
7.9,
6.3,
6,
7.2,
5.1,
7.3,
8,
6.2,
6,
6.7,
8.1,
6.4,
8,
6.3,
6.4,
6.6,
6.4,
6,
6.6,
5.9,
6.4,
6.3,
7.3,
6.8,
7.2,
5.7,
6,
6.5,
5.8,
5.8,
6.7,
7.8,
5.6,
5.8,
7.4,
6.9,
5.5,
6.3,
4.7,
5.6,
6.4,
4.2,
6.4,
7.7,
6.7,
7.7,
5.7,
7.6,
6.4,
5.6,
6.8,
2.4,
6.2,
5.9,
7.1,
7.6,
5.5,
7,
7.1,
7.4,
7.6,
5.9,
5.9,
8,
7.4,
5.8,
6.3,
5.7,
5.1,
7.6,
6.4,
7.4,
8.2,
6.5,
5.5,
6.5,
5.6,
4.6,
7.9,
7.1,
6.9,
7.3,
7,
7.7,
6.7,
6.3,
5.8,
7.1,
7.3,
6.4,
7.1,
7.6,
6.8,
6.6,
6.7,
6.1,
6,
7.6,
7.1,
5,
6.2,
5.6,
7.4,
5,
5.2,
7.6,
6.6,
7,
5.7,
8.2,
6.2,
6.6,
4.7,
6.3,
6.1,
6.7,
6.1,
7,
7.4,
7.3,
5.8,
6.7,
5.8,
7.8,
6.6,
6.5,
6.7,
7.3,
5.8,
5.5,
6.3,
7.4,
5.9,
6.2,
5.9,
6.5,
4.4,
3.5,
6.6,
6,
6.4,
6.5,
4.3,
4.2,
6.5,
6.1,
6.3,
6.2,
5.9,
5.9,
6.5,
6.4,
6.5,
5.7,
8,
7.3,
6.7,
7.5,
5.4,
6.6,
7.7,
5.8,
5.6,
6,
6.2,
5.9,
5.1,
6.8,
6,
5.1,
5.8,
6.2,
6.4,
4.8,
4.9,
5.6,
5.5,
3.7,
5.9,
6.3,
7.6,
8.3,
6.9,
6.7,
6.8,
7.1,
6.4,
6.4,
7.4,
6.4,
6,
6.5,
7.8,
6,
7,
6,
6.1,
6.8,
6.4,
4.5,
5.8,
6.3,
5.7,
7.2,
7.6,
4.7,
6.6,
6.8,
7.3,
4.8,
6.3,
5.5,
6.2,
5.8,
5.7,
6.5,
6.7,
7.4,
6.9,
5.5,
8.1,
7.7,
7.3,
5.2,
7.1,
7.1,
7.2,
6.5,
4.6,
5.6,
7.7,
7.2,
6.8,
5.4,
6.3,
5.6,
6.8,
4.3,
6.3,
6.5,
6.4,
6.3,
5.9,
6.5,
6.5,
6.1,
5.9,
6.6,
7.4,
7.3,
6.6,
5.6,
5.3,
6,
5.4,
6.8,
6.4,
7.1,
4.9,
5.8,
7.1,
7.2,
6,
6,
7,
5.4,
6.5,
6.4,
4.9,
6.3,
7.7,
7.8,
5.5,
7.5,
6.4,
5.6,
7.5,
6.8,
6.8,
6,
7.3,
6,
7,
5.1,
6.8,
6.5,
6.6,
7.2,
7,
7,
5.9,
5.4,
6.6,
7,
6.5,
6.3,
6.5,
6.5,
5.8,
6.6,
5.4,
6.1,
4,
7.6,
7.9,
5.3,
6.6,
6.3,
7.2,
7,
6.9,
5.2,
8.1,
6.6,
6.2,
7.2,
7.3,
6.7,
6.4,
7.8,
6.4,
4.1,
4.1,
7.4,
5.8,
7.6,
7.2,
7.8,
7.7,
6.4,
5.1,
5.5,
7.4,
6,
7.5,
7,
7.5,
7.3,
5.7,
7.3,
7.2,
5.9,
7.8,
7.7,
8.1,
6.6,
7.1,
5.9,
8,
4.6,
6.1,
6.4,
6,
5.2,
7.6,
6.4,
6.1,
6.1,
5.2,
7.7,
7.3,
6.9,
8.5,
6.3,
5.9,
7.8,
6.7,
6.4,
5.9,
6.6,
6.8,
6.5,
6.6,
5.8,
6.9,
7.1,
5.8,
7.2,
6,
4.7,
5.2,
5.5,
7,
5.8,
6.2,
6.5,
7.2,
5.1,
4.7,
5.9,
5.8,
7.2,
6.2,
5.7,
6.1,
6,
6.9,
6.5,
5,
5.7,
7,
5.1,
5.3,
4.4,
4.7,
6.7,
6.7,
5.7,
7.4,
6.1,
6.4,
6.2,
6.2,
5.9,
4,
6.2,
4.6,
6.4,
5.9,
5.1,
7.6,
4.2,
7.8,
5.8,
5.9,
8.4,
4.8,
6.2,
6.5,
6.3,
3.3,
5.9,
5.8,
4.7,
4.1,
6.8,
6.2,
4.5,
5.8,
7.3,
5.9,
4.4,
5.8,
5.1,
6.9,
6.2,
6.9,
7.3,
7.1,
6,
7,
7.6,
7.1,
6.7,
7,
8,
5.3,
4.9,
6.4,
6.1,
6.5,
5.7,
5.1,
6.6,
6.5,
6.9,
7.6,
5.6,
6.2,
4.4,
5.6,
5.5,
6.7,
6.1,
6.2,
7.3,
6.6,
8.2,
6.4,
6.4,
5.2,
6.5,
7.1,
7.3,
5.2,
7.7,
7.6,
5.7,
7,
6,
8.1,
8,
5.6,
6.1,
6.9,
5.2,
7,
6.3,
7,
6.9,
6.2,
6.4,
6.4,
5.7,
6.1,
5.4,
6.7,
6.8,
6,
7.8,
5.3,
4.5,
5.4,
7.8,
7.2,
6.6,
7.6,
5.9,
6.7,
7.7,
5.4,
6.9,
7.7,
6.8,
6.4,
5.7,
7.3,
6.8,
6.3,
5.9,
7.4,
8.3,
6.2,
6.3,
5.8,
7.5,
6.3,
6.4,
7.2,
6.3,
6.9,
6.6,
6,
7.5,
7.7,
6.2,
5.4,
6.6,
5.3,
5.6,
5.9,
7.8,
6.7,
7.4,
6.2,
5.4,
6.7,
5.3,
5.9,
4.8,
3.8,
8.5,
6.8,
5.3,
7.3,
6.6,
6.2,
5.2,
6.2,
6.2,
6.6,
6.2,
5.1,
6.6,
6.1,
6.6,
5.9,
6.3,
7.1,
5,
5.6,
7.4,
4.5,
6.2,
5,
6.5,
5.1,
6.5,
6.2,
6.3,
3.8,
6.2,
5.7,
6.7,
6.8,
6,
7.3,
5.5,
6.7,
4.8,
5.7,
5.1,
6,
4.2,
7.4,
4.6,
6.9,
6.9,
8,
6.4,
6.3,
6.8,
6.8,
5.4,
7.2,
7.3,
5.2,
5.5,
7.7,
7.1,
5.3,
5.6,
5.7,
7.1,
7.6,
5.5,
5.1,
4.9,
6.5,
5.6,
5.3,
6.5,
6.8,
6.5,
6,
8.4,
6,
7.6,
6.9,
6.4,
5.1,
7,
5.7,
6.8,
6.7,
6.2,
7.2,
6.2,
5.6,
4.4,
7.5,
7.1,
6.4,
7.1,
6.9,
7.5,
6.3,
6.4,
5.9,
6.8,
6.3,
3.6,
5.3,
5.9,
6.9,
6.9,
6.1,
8.5,
6.3,
7.3,
6.3,
7.2,
7.3,
6.3,
8.1,
6.9,
6.3,
7.3,
5.5,
6.1,
6.9,
7.2,
6.4,
6.4,
8.3,
7.2,
6.8,
6.5,
7.8,
7.6,
7.2,
6.7,
6.8,
6.3,
6.2,
6.2,
8.6,
8,
7,
8,
8.1,
6.7,
7.9,
6.1,
4.2,
6.1,
6.6,
7.5,
7.4,
7.2,
6.9,
7.4,
5.4,
6.8,
6.3,
7.2,
6.9,
6,
5.9,
5.4,
5.9,
6.1,
7.7,
5.8,
7.6,
6.1,
5.4,
5.1,
6.4,
6.3,
7.5,
7.1,
7.8,
6.5,
6.6,
7.4,
7.6,
7.5,
6.6,
7.2,
7.6,
6.2,
5.6,
7.6,
6.6,
7,
2.7,
7.6,
6.6,
6.9,
6.8,
3.7,
6.1,
5.9,
6.7,
6.9,
5.5,
7.1,
7.1,
7.3,
3.4,
6.8,
6.9,
7,
5.5,
5.1,
6.2,
5.9,
5.2,
6.2,
5.5,
7.4,
4.4,
6.3,
6.1,
5.3,
5.4,
6.7,
5.9,
7.3,
5.5,
5.8,
4.6,
6.7,
5.1,
5.6,
7,
6.4,
6.7,
4.1,
5.5,
2.7,
6.4,
4.8,
6.1,
4.8,
7,
6.8,
5.6,
6.1,
7.9,
8.4,
6.5,
7.1,
6.6,
7,
5.6,
4.8,
7.5,
6,
6.8,
6.5,
7.9,
6.4,
5.8,
7.7,
5.3,
5.3,
7.5,
6.9,
4.9,
7.1,
8,
7.9,
7.6,
5.9,
6.3,
6.4,
8.2,
6.9,
7.8,
6.7,
7.5,
7.4,
5.2,
7.6,
7.3,
6.6,
6.8,
6.9,
5.8,
6.6,
6.7,
6.7,
6.3,
7.7,
6.1,
4.9,
6.2,
7.8,
8.2,
6.9,
6.2,
6.9,
4.8,
8,
5.3,
6.7,
5.4,
5.4,
4.9,
6.1,
5.8,
7,
6.5,
6.6,
5.7,
6.6,
7,
7.4,
5.3,
7.4,
7.4,
6.8,
7.2,
6,
7.8,
6.6,
7.9,
5.7,
7.1,
5.6,
7.8,
7.9,
6.9,
7.7,
6.9,
6,
6.2,
5.9,
6.8,
3.6,
6.7,
6.3,
6.4,
6.4,
5.7,
6.2,
5.2,
6.1,
7.1,
7.2,
6.5,
6,
7,
7,
7.5,
6.6,
7.4,
6.5,
6.2,
7.8,
5.2,
6.5,
6.5,
5.2,
7.2,
7.1,
4.5,
5.7,
6,
6.4,
5.2,
4.3,
6.1,
6.8,
5.2,
6.5,
7.5,
7.1,
6.9,
8,
8.2,
6.4,
7.9,
6.7,
6.1,
8.9,
8.1,
6.2,
4.9,
5.8,
6,
7,
6,
7.9,
8.1,
6.2,
6.7,
7.3,
4.6,
6.1,
6.2,
7.8,
6.1,
5.8,
6.5,
7.2,
7.8,
4.7,
6.8,
5.9,
7.2,
8.7,
5,
6.6,
8.3,
6.7,
7.8,
6.5,
6.1,
8.1,
5.2,
5.6,
5.8,
6.6,
6.6,
5.5,
7,
6.5,
5.8,
5.6,
5.6,
5.8,
7.6,
6.4,
6.3,
4.6,
6.5,
7.5,
7.5,
5.3,
7.5,
3.3,
3.5,
9.3,
4.8,
6.9,
6,
7.3,
6.6,
7.5,
6.9,
6.8,
6.3,
6.4,
5.6,
6.3,
7.3,
6.6,
4.6,
5.1,
5.6,
5.3,
5.6,
5.9,
4.7,
4.8,
6.8,
5.4,
5.1,
7,
4,
7.3,
6.8,
7,
7.1,
6.9,
7.3,
8.2,
7.1,
7.7,
6.5,
4.9,
6.4,
5.9,
6.2,
5.8,
6.7,
5.9,
7.3,
4.1,
4.9,
7.9,
5.6,
5.2,
4.1,
6.6,
2.9,
6.5,
7.2,
6.8,
7.8,
6.7,
7.1,
5.7,
5.3,
7.7,
6.1,
7.3,
7.2,
5.3,
6.1,
5.8,
5.7,
6.7,
6.5,
7.2,
7.6,
4.6,
6.9,
6.6,
6.3,
6.2,
5.3,
7.3,
5.6,
6.2,
5.2,
5.3,
5.4,
4.9,
5.5,
6.7,
3.9,
7.2,
5.1,
6.5,
8.2,
7.7,
7.2,
6.1,
8.8,
6.8,
6.8,
6.7,
7.1,
7.1,
6.1,
8,
7.5,
6.6,
5.4,
6.1,
6.1,
5.6,
5.8,
2.8,
6.7,
5.1,
7.2,
6,
6.7,
6.2,
6.2,
6.8,
7.1,
7.1,
7,
7.1,
6.4,
7,
6.2,
7.5,
4.8,
7.3,
5.8,
7.6,
5.6,
7,
6.6,
6.5,
7.4,
4.6,
6.4,
6,
5.9,
6.4,
6.6,
6.9,
6.9,
5.8,
6.4,
5.3,
6.5,
5.7,
6.7,
3.9,
4.1,
6.2,
3.8,
5.1,
7.8,
7.8,
6.1,
5.8,
6.3,
5.4,
7.3,
6.8,
7.3,
6.5,
7.2,
6.3,
5.9,
7.8,
7.4,
4.8,
6.3,
7.8,
7.5,
6.8,
6.6,
4.6,
7.1,
6.1,
6.7,
7.1,
5.8,
6.7,
5.8,
6.8,
8.5,
6.6,
7.7,
4.7,
6.4,
5.5,
8.6,
7,
7.1,
5.7,
3.7,
7.5,
4.6,
4.9,
6.9,
7.1,
5.8,
5.4,
7.3,
7.1,
5.8,
8.1,
5.7,
4.4,
7.9,
7.6,
4.8,
6.7,
2.7,
5.8,
7.5,
5.4,
4.1,
5.9,
6.3,
6.8,
2.3,
6.9,
8.1,
6.1,
5,
5.5,
6.2,
6.2,
6.3,
6.7,
3.5,
7.5,
6.6,
7.5,
7.2,
4.8,
6.6,
3.5,
7.6,
6.3,
5.5,
6.3,
6.5,
6.9,
7.6,
3.9,
6.1,
7.3,
8.3,
5.8,
6.8,
7,
5.9,
6.5,
6.4,
5.8,
5.1,
6.8,
5.3,
5.3,
4.9,
6.8,
7.1,
6.1,
8.5,
5.9,
6.3,
5.9,
5.4,
6.9,
7.5,
8.2,
5.9,
5,
7.3,
6.4,
6.6,
7.8,
4,
7.6,
7.7,
5.8,
5.2,
5.6,
5.3,
6.6,
1.9,
5.7,
6.6,
6,
6.1,
4.8,
6.2,
7.5,
6.3,
7.1,
6.6,
6.1,
6.7,
5.6,
7.2,
4.3,
6.4,
7.1,
6.3,
7.4,
6.1,
6.6,
6,
6.8,
6.8,
7.2,
1.9,
5.5,
4.5,
6.3,
6.7,
2.8,
5,
4.3,
5.6,
6.2,
5.3,
7.4,
7.4,
6.5,
7.1,
7.2,
2.3,
6.4,
6.1,
7,
7,
7,
4.9,
6.9,
7.5,
6.9,
4.5,
7.4,
7,
2.8,
7.5,
7.1,
6.4,
6.7,
5.3,
6.2,
6.4,
5.1,
5.5,
5.4,
7.5,
7.4,
8,
5.7,
6.8,
5.9,
7.2,
5.5,
8.5,
5.6,
4.1,
6.1,
5.4,
7.1,
3.6,
6.5,
8.6,
7,
7.6,
6.5,
6.4,
6.3,
5.7,
6.3,
6,
7.7,
6.2,
7.7,
6.4,
6.4,
6.9,
7.3,
7.3,
6.2,
6.6,
6.7,
5.7,
3.1,
6.3,
5.7,
7.1,
7,
6.1,
6.6,
7.8,
8.3,
3.9,
7,
6.7,
7.3,
6.3,
7.8,
7.3,
7.6,
5.3,
7.9,
5.3,
6.8,
7.1,
5.8,
5.8,
8.3,
5.6,
6.8,
5,
7.6,
6.7,
6.7,
5.7,
5.2,
7.5,
7.2,
5.3,
6.5,
5,
6.1,
4.4,
7.5,
5.7,
5.5,
7.1,
5.9,
6.7,
7,
7.9,
6.9,
7.3,
7.3,
3.5,
7.8,
6.7,
6.4,
7.1,
7.8,
5.9,
7.2,
6.2,
6.7,
7.6,
6.2,
6.5,
8.1,
6.3,
4.4,
6,
7.6,
8.4,
7.9,
5.6,
6.5,
7.5,
6.3,
7.9,
5.1,
6.7,
6.7,
5.6,
5.6,
6.8,
6.2,
5.6,
6.4,
5.6,
7.4,
7.2,
4.9,
7.5,
4.8,
3.1,
5.8,
6.7,
6.5,
5.9,
5.5,
3.6,
7.4,
3,
7.6,
6.4,
6.9,
6.6,
5.5,
4.1,
6.8,
6.5,
7.4,
7.7,
7.1,
6.3,
7.6,
8,
7.3,
7.6,
7.8,
6.5,
6.4,
8,
4.8,
7.8,
5.9,
5.4,
3.3,
8.2,
5.4,
6.4,
4.8,
5.9,
5.5,
7.9,
4.9,
7.2,
5.3,
7.2,
5.1,
5.6,
7.6,
7.2,
5.7,
5.2,
7.7,
7,
6,
6.6,
6.8,
7.2,
7.2,
2.8,
6.6,
6.7,
7,
4.4,
6.2,
7.3,
5.1,
6.6,
4.5,
5.9,
6.6,
6.5,
7.3,
7.5,
5.9,
7.4,
6.9,
7.9,
8.4,
8,
6,
6.8,
7.8,
8.1,
6.1,
6.2,
6.2,
7.4,
6.6,
7.3,
7.5,
5.6,
7.3,
6.4,
5,
5.4,
7.1,
5.3,
6.5,
6.2,
6.4,
6.9,
5.7,
7.7,
5.4,
5.6,
7.7,
5.1,
6.8,
8.4,
4.9,
7.1,
6.6,
6.1,
4.1,
5.8,
8.1,
7.6,
7.8,
4.6,
6,
7,
6.7,
6.4,
7.2,
7.4,
4.8,
4,
6.2,
7.7,
6.7,
7.9,
7.9,
5.5,
6.2,
5.1,
4.1,
6.7,
4.7,
6.4,
6.3,
5.5,
7.3,
6.3,
4.9,
7.6,
6,
6.2,
6.8,
4.5,
5.7,
4.6,
6.2,
7,
6.9,
6.7,
5.6,
6.6,
6.4,
2.8,
5.4,
5,
5.1,
8,
5.9,
8.2,
7,
6.6,
6.7,
5.5,
4.9,
6.9,
5.6,
8,
5.3,
6.2,
5.3,
6.6,
7.2,
4.6,
7.5,
6.5,
7.6,
6.2,
8,
6.3,
7.2,
6.7,
5.3,
6.3,
6.5,
8.3,
7.2,
6.8,
6.4,
6.9,
6.2,
6.1,
5.1,
4.5,
5.9,
8.1,
5.7,
6.8,
7.5,
8.3,
7.4,
8,
6.9,
6.9,
5.5,
7.2,
6.9,
5.5,
5.2,
7.1,
5.5,
6.7,
5,
6.4,
6.6,
5.9,
5.7,
4.5,
5,
4.6,
6.5,
4.9,
6,
6.9,
5.7,
6.9,
4.4,
7,
5.4,
5.4,
7.6,
5.9,
6.6,
6.7,
3.9,
5.7,
6.5,
6.8,
7.3,
7,
6.5,
7.7,
7.7,
5.9,
6.8,
7.4,
5.1,
7.4,
7.2,
8.3,
8.1,
7.3,
3.6,
1.6,
8,
6.2,
9,
6.1,
5.7,
6.8,
5.5,
6.8,
7.3,
6.1,
7.2,
5.9,
6.1,
6.8,
7.7,
4.9,
6.1,
2.5,
6.1,
5.9,
5.7,
5.6,
7.2,
7.7,
7.8,
6.1,
5.8,
6.5,
7.9,
6.3,
3.8,
8.3,
6.4,
6.7,
6.1,
6,
5.8,
5.6,
6.1,
5.9,
7.3,
6.8,
5.7,
7.3,
6.3,
5.9,
7.1,
7.1,
8,
5.1,
7.1,
6.5,
4.5,
6.6,
4.3,
6.7,
5.4,
6.6,
7.3,
6.9,
8,
7.8,
6.1,
5.1,
7.4,
7.8,
8,
6.7,
6.6,
6.4,
6.7,
6.2,
7.3,
8.1,
7,
8,
8,
7,
7.9,
5.9,
6.6,
6.3,
7.7,
6.9,
7.1,
7.4,
6.5,
6.5,
6.8,
7.5,
6.6,
7.1,
6.6,
7,
3.3,
6.7,
6.8,
6,
5.4,
4.3,
6.2,
7.7,
8,
7.4,
5.9,
7.8,
7.4,
6.5,
7,
7.6,
6.9,
5.3,
6.4,
7.8,
6.7,
5.3,
6.3,
7,
6.6,
8.4,
5.4,
7.8,
7.6,
6.6,
6.4,
7,
5.7,
5.9,
6.3,
6.3,
6.2,
2.1,
5,
5.3,
7.1,
7,
7.1,
7,
7.7,
7.1,
6.8,
7.5,
6.3,
7.3,
6.8,
7.2,
6.4,
6,
6.4,
7.5,
4.6,
7.7,
6.7,
5.6,
7.5,
5.8,
8.3,
6.6,
7.2,
8.7,
6,
8,
4.5,
7.9,
7.5,
6.8,
7.2,
7.1,
7.4,
7.6,
6.9,
6,
7.3,
4.6,
6,
5.5,
7.5,
6.3,
5.1,
6.8,
5.3,
7.3,
7.3,
7.1,
7.6,
5.3,
7.8,
7.7,
7.7,
5.4,
6.2,
7.4,
6.2,
5.1,
6.8,
7.4,
5.8,
6.4,
6.9,
5.5,
5.4,
8.3,
7.9,
6.5,
6.4,
5.8,
6.6,
8.3,
6.2,
6.9,
5.9,
6.1,
5.8,
7.3,
5.9,
5.5,
5,
7,
6.4,
5.9,
7,
6.1,
6.9,
7.5,
7.3,
6.5,
6.2,
6,
6.3,
5.8,
6.1,
6.9,
5.4,
6.7,
7.4,
5.6,
6.5,
6.5,
5.8,
5,
5.5,
6.5,
7.2,
5.2,
5.7,
4.7,
5.9,
6.8,
5.9,
7.7,
4.4,
6.6,
6.7,
5.5,
6.5,
6.2,
7.1,
6.1,
6,
7.4,
5.9,
4.1,
5.9,
7,
6.8,
7.4,
7.1,
7,
5.8,
7.8,
6.5,
7,
6.3,
5.3,
5.5,
7.4,
4.3,
6,
5.2,
6.7,
8.6,
6.1,
5.8,
7.7,
8,
5.6,
6.7,
6.6,
4.1,
7.3,
7.1,
6.5,
7,
5.5,
6.6,
7.1,
7.9,
7.1,
5.6,
7.3,
3.3,
6.5,
4.8,
5.2,
6.3,
7.2,
6.8,
5.7,
7.2,
6.9,
6.2,
6.7,
6.5,
7.2,
5.3,
6.7,
3.6,
5.7,
7.3,
5,
6.6,
7.3,
6.2,
6.6,
6.3,
3.3,
6.2,
3.5,
5.5,
5.9,
4.7,
3.9,
6.1,
6.7,
7.3,
6.7,
6.1,
6.9,
7.9,
4.5,
7.6,
7.5,
7.1,
6.9,
8.5,
7.5,
6.6,
8,
7,
6.8,
6.7,
6.5,
8,
6.5,
4.9,
7.1,
7,
7,
4.5,
7.7,
6.7,
7,
6.5,
6.2,
5.7,
6.4,
5.4,
6.1,
7.6,
6.2,
6.6,
7.3,
4.2,
6.5,
6.5,
5.7,
7.3,
6.9,
5,
7.3,
6.5,
2.1,
7,
8,
6.9,
7.1,
7.2,
6.7,
8.9,
7.9,
5.6,
8,
6.2,
7.9,
8.1,
7.6,
3.5,
7.6,
6.5,
5.6,
7.7,
5.2,
6.1,
7.4,
6.8,
6.4,
5.7,
6.7,
5.6,
7,
7.6,
6.5,
6.3,
7.1,
7.1,
6.9,
5.4,
5.1,
5.3,
7.3,
7.3,
7.1,
6,
6.6,
7.2,
7.2,
6.9,
6.8,
7.7,
7.4,
6.5,
6.4,
5.6,
6.8,
5.5,
6.9,
6,
6.4,
6.6,
5.3,
6.9,
6.5,
7.4,
6.9,
6.7,
7.6,
5.4,
7.3,
6,
7.2,
6,
3.1,
6.9,
6.2,
6.3,
6.7,
8,
7,
7.2,
6.2,
3.5,
7.5,
6.7,
9.2,
6.1,
7.7,
7.6,
6.1,
4.9,
6.8,
7,
5.7,
7.3,
7.5,
7.4,
7.2,
6.8,
6.8,
5.2,
7.2,
4,
6.8,
6.9,
7.3,
6.1,
7.8,
6,
7,
7.1,
6.2,
6.9,
7.6,
7.6,
6.4,
6.2,
7.5,
2,
6.2,
6.5,
6.8,
6.3,
6.3,
6.6,
6.4,
7.5,
6.5,
7.2,
6.3,
7,
6.3,
2.3,
7.1,
6.2,
6.7,
6.5,
5.9,
6,
6.9,
7.3,
7.7,
7,
6.4,
5.6,
8.2,
6.5,
8.1,
5.4,
6.3,
7.8,
6.8,
7.1,
6.2,
7.3,
5.9,
3.6,
7.7,
7.3,
7.4,
6.6,
6.9,
6.8,
7.2,
7.7,
8.1,
7.7,
7.6,
7.2,
7.2,
8.1,
7.5,
8.1,
7.8,
7.8,
5.8,
7.6,
7.4,
6.3,
6.9,
8.6,
5.1,
6.4,
7.9,
6.9,
7.5,
7.2,
5.8,
2.9,
6.2,
6.8,
6.1,
7.7,
5.2,
6.8,
7,
5.9,
7.1,
5.5,
7.4,
7.3,
4.6,
7.2,
5.1,
6.7,
5.3,
7.8,
6.7,
7.2,
5.8,
7,
3.8,
5.7,
6.7,
6.1,
6.2,
6.2,
4.7,
6.3,
7.3,
5.8,
6.1,
7.1,
7.1,
6.7,
6.9,
2.1,
6.6,
8.3,
7.2,
5.6,
7.7,
6.6,
7.4,
7.1,
7.9,
6.7,
6.6,
7.9,
4.9,
7.2,
6.1,
5.3,
5,
7.6,
7.6,
6.6,
6.6,
7.3,
6.6,
6.9,
5.8,
4.4,
6.6,
7.1,
7.6,
4.6,
6.8,
4.9,
7.3,
5,
8,
5.2,
8.5,
6.5,
7.4,
7.7,
7.4,
5.1,
5,
7.2,
6.4,
5.6,
6.1,
5.2,
7.3,
7.5,
4.5,
6.6,
5.3,
4.9,
7.7,
8,
3.8,
7.6,
5.9,
6.2,
7.2,
6.3,
5.2,
6.9,
6.8,
3.5,
6.1,
4.5,
5.9,
6.9,
7.7,
5.3,
7,
6.6,
6.4,
7.9,
7.7,
7.2,
6.8,
7.4,
4.6,
6.4,
7,
7.7,
6.8,
7,
7,
6.3,
7.1,
4.4,
7.1,
6.1,
7.3,
6.2,
6.2,
6.2,
3.3,
7.5,
7.4,
8,
5.9,
6.8,
7.4,
6.7,
5.5,
5.7,
7.2,
5.9,
6.7,
7.1,
7.7,
7.4,
8.4,
5.4,
8.1,
7.8,
6.8,
6.5,
7.3,
5.9,
8.7,
5.8,
6.1,
7.6,
5.8,
6.5,
7.3,
6.2,
5,
7.8,
8.1,
6.7,
6.1,
7.1,
5.6,
7.6,
4.6,
7.1,
7.3,
4,
8,
6.7,
5.7,
4.6,
4,
7,
5.9,
7.5,
4.7,
6.7,
6.7,
7.1,
2.7,
7.3,
7.6,
5.8,
6.5,
6.6,
6.9,
8.5,
4.8,
7,
5.4,
6.9,
6.6,
5.9,
6.3,
6.3,
7.7,
7,
6.3,
5.9,
6.2,
7.7,
6.5,
5.8,
6.1,
5.2,
8.2,
6,
6.8,
7,
6.8,
7.1,
6.9,
6.9,
6.9,
7.2,
7.8,
7.3,
7.5,
6,
6.8,
3.9,
6.1,
7.5,
8.2,
7.8,
5.2,
6.8,
7,
6.5,
5.7,
6.4,
5.3,
4.7,
7.6,
7.1,
6.5,
8.5,
8.7,
7.1,
8.3,
7.4,
6.4,
7.5,
7.2,
7.6,
7.8,
8.2,
6.6,
5.7,
7.4,
8,
5.4,
7.4,
5.7,
6.8,
5.4,
5.1,
5.9,
8.2,
5.3,
4.3,
7.2,
5.9,
3,
7.9,
3.2,
6.5,
7,
6.9,
4.4,
6,
5.3,
5.3,
7.1,
5.4,
6.9,
7.3,
6.6,
5.4,
8.4,
6.3,
6.1,
5,
7.2,
5.3,
5.3,
6,
7.4,
5.9,
4.1,
6.7,
5.8,
6.5,
5.9,
7,
8,
6.5,
6.4,
6.8,
7.4,
8.3,
5.3,
8.1,
8,
5.7,
7.1,
7.8,
5.9,
7.8,
6,
5.3,
7.2,
5.1,
5.1,
6.9,
4.6,
6.7,
7.1,
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\n",
" \n",
" \n",
" \n",
" \n",
"
"
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"source": [
"## Cast likes\n",
"fig = px.scatter(df, x=\"cast_total_facebook_likes\", y=\"imdb_score\", hover_name='movie_title')\n",
"fig.update_traces(marker_color='blue', marker_line_color='blue', opacity=0.6)\n",
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"### e. Reviews"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This figure below shows the distribution of the IMDb reviews from critics in the form of a box plot."
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
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"Hook ",
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"Ballistic: Ecks vs. Sever ",
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"Escape Plan ",
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"The Sum of All Fears ",
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"The Last Legion ",
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"What Women Want ",
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"You've Got Mail ",
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"Focus ",
"This Means War ",
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"The Long Kiss Goodnight ",
"Proof of Life ",
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"We Are Marshall ",
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"I, Frankenstein ",
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"Perfume: The Story of a Murderer ",
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"U-571 ",
"The American President ",
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"300 ",
"Meet the Fockers ",
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"Just Like Heaven ",
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"Did You Hear About the Morgans? ",
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"Me, Myself & Irene ",
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"How to Lose a Guy in 10 Days ",
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"16 Blocks ",
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"28 Days ",
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"15 Minutes ",
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"21 ",
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"30 Minutes or Less ",
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"How to Lose Friends & Alienate People ",
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"Nothing to Lose ",
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"There's Something About Mary ",
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"How Stella Got Her Groove Back ",
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"This Is Where I Leave You ",
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"A Very Harold & Kumar 3D Christmas ",
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"You Again ",
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"Daybreakers ",
"She's Out of My League ",
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"Stay Alive ",
"Quigley Down Under ",
"Alpha and Omega ",
"The Covenant ",
"Shorts ",
"To Die For ",
"Nerve ",
"Vampires ",
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"My Best Friend's Girl ",
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"Control ",
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],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequential": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"sequentialminus": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "Number of Critic Reviews"
},
"xaxis": {
"anchor": "y",
"domain": [
0,
0.98
]
},
"yaxis": {
"anchor": "x",
"domain": [
0,
1
],
"title": {
"text": "num_critic_for_reviews"
}
}
}
},
"text/html": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Voted\n",
"fig = px.histogram(df, x=\"num_voted_users\")\n",
"fig.update_traces(marker_color='blue', marker_line_color='blue', opacity=0.6)\n",
"fig.update_layout(title_text='Number of IMDb Users Who Voted')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Correlation Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Correlation is a measure of relationship strength between two variables. If two variables are highly positively correlated, then an increase in one will generally lead to an increase in the other. If two variables are highly negatively correlated, then an increase in one will generally lead to a decrease in the other. The correlation coefficients for each of the x variables with the y variable, IMDb score, are shown below."
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"imdb_score 1.000000\n",
"num_voted_users 0.479882\n",
"duration 0.370148\n",
"num_critic_for_reviews 0.349811\n",
"num_user_for_reviews 0.324998\n",
"movie_facebook_likes 0.283770\n",
"profit 0.255117\n",
"gross 0.218389\n",
"director_facebook_likes 0.191684\n",
"content_rating 0.120074\n",
"cast_total_facebook_likes 0.106502\n",
"actor_2_facebook_likes 0.101541\n",
"actor_1_facebook_likes 0.093774\n",
"actor_3_facebook_likes 0.065357\n",
"budget 0.038711\n",
"facenumber_in_poster -0.069214\n",
"color -0.118699\n",
"title_year -0.133974\n",
"from_USA -0.135822\n",
"in_english -0.169427\n",
"Name: imdb_score, dtype: float64"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Correlation with IMDb score\n",
"df[df.columns[0:]].corr()['imdb_score'][:].sort_values(ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"## Correlation heatmap\n",
"plt.figure(figsize=(14,10))\n",
"corr = df.corr()\n",
"mask = np.zeros_like(corr)\n",
"mask[np.triu_indices_from(mask)] = True\n",
"sns.heatmap(df.corr(), mask=mask, vmax=.8, square=True, annot=True, fmt=\".2f\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The variables that are the highest determinants of overall IMDb score are likely the ones that are highly correlated with IMDb score. Interestingly, there are no variables that have a strong correlation, so the variables that are 'highly' correlated will have to be based on the context of the other variables. In this case, the variables that we will deem to be 'highly' correlated (above 0.2) are:\n",
"* num_voted_users\n",
"* duration\n",
"* num_critic_for_reviews\n",
"* num_user_for_reviews\n",
"* movie_facebook_likes\n",
"* profit\n",
"* gross\n",
"\n",
"These findings are generalized and the extent of the variables' likelihood to affect IMDb score is based on the correlation coefficient as mentioned above. So now that we have this information, what can we do with it? The next step is to set up a regression model based on the determinants to predict IMDb score."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first step in regression is setting x and y variables. The y variable, the variable we're trying to predict, is IMDb score. The x variables, or determinants of the y variable, will be all the numerical columns aside from IMDb score."
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"## Set X and y variables\n",
"y = df1['imdb_score'] \n",
"X = df1.drop(['movie_quality', 'imdb_score'], axis =1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### a. Scikit-learn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first model we'll build and test is Scikit-learn's linear regression algorithm. "
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"## Build regression model based on scikit-learn algorithm\n",
"model1 = lm.LinearRegression()\n",
"model1.fit(X, y)\n",
"model1_y = model1.predict(X)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('title_year', '-0.017'),\n",
" ('content_rating', '0.037'),\n",
" ('duration', '0.011'),\n",
" ('from_USA', '-0.205'),\n",
" ('in_english', '-0.701'),\n",
" ('color', '-0.335'),\n",
" ('gross', '-0.000'),\n",
" ('budget', '-0.000'),\n",
" ('profit', '0.000'),\n",
" ('movie_facebook_likes', '-0.000'),\n",
" ('director_facebook_likes', '0.000'),\n",
" ('actor_1_facebook_likes', '0.000'),\n",
" ('actor_2_facebook_likes', '0.000'),\n",
" ('actor_3_facebook_likes', '0.000'),\n",
" ('cast_total_facebook_likes', '-0.000'),\n",
" ('num_critic_for_reviews', '0.003'),\n",
" ('num_user_for_reviews', '-0.001'),\n",
" ('num_voted_users', '0.000'),\n",
" ('facenumber_in_poster', '-0.025')]"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Display coefficients for each variable\n",
"coef = [\"%.3f\" % i for i in model1.coef_]\n",
"xcolumns = [ i for i in X.columns ]\n",
"list(zip(xcolumns, coef))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The strength or accuracy of a regression model is evaluated through mean square error (MSE) and R-squared. The goal of a regression model is to minimize MSE and maximize R-squared. A lower MSE means less error in the model."
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean Square Error: 0.6492138737974602\n",
"Variance or R-squared: 0.4164775382245356\n"
]
}
],
"source": [
"## Model evaluation\n",
"print(\"Mean Square Error: \", mean_squared_error(y, model1_y))\n",
"print(\"Variance or R-squared: \", explained_variance_score(y, model1_y))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### b. Statsmodel"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we'll test Statsmodel regression algorithm based on ordinary least squares regression."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: imdb_score R-squared: 0.416\n",
"Model: OLS Adj. R-squared: 0.414\n",
"Method: Least Squares F-statistic: 158.4\n",
"Date: Tue, 26 May 2020 Prob (F-statistic): 0.00\n",
"Time: 13:11:29 Log-Likelihood: -4564.7\n",
"No. Observations: 3794 AIC: 9165.\n",
"Df Residuals: 3776 BIC: 9278.\n",
"Df Model: 17 \n",
"Covariance Type: nonrobust \n",
"=============================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"---------------------------------------------------------------------------------------------\n",
"Intercept 40.5584 3.229 12.560 0.000 34.227 46.889\n",
"title_year -0.0173 0.002 -10.713 0.000 -0.020 -0.014\n",
"content_rating 0.0368 0.017 2.105 0.035 0.003 0.071\n",
"duration 0.0112 0.001 16.637 0.000 0.010 0.012\n",
"from_USA -0.2021 0.036 -5.683 0.000 -0.272 -0.132\n",
"in_english -0.7014 0.073 -9.603 0.000 -0.845 -0.558\n",
"color -0.3387 0.076 -4.486 0.000 -0.487 -0.191\n",
"gross -1.876e-06 1.49e-07 -12.561 0.000 -2.17e-06 -1.58e-06\n",
"budget 1.871e-06 1.49e-07 12.527 0.000 1.58e-06 2.16e-06\n",
"profit 1.877e-06 1.49e-07 12.570 0.000 1.58e-06 2.17e-06\n",
"movie_facebook_likes -1.927e-06 9.09e-07 -2.119 0.034 -3.71e-06 -1.44e-07\n",
"actor_1_facebook_likes 5.629e-05 1.26e-05 4.484 0.000 3.17e-05 8.09e-05\n",
"actor_2_facebook_likes 5.986e-05 1.32e-05 4.534 0.000 3.4e-05 8.57e-05\n",
"actor_3_facebook_likes 5.493e-05 2.04e-05 2.687 0.007 1.48e-05 9.5e-05\n",
"cast_total_facebook_likes -5.385e-05 1.25e-05 -4.301 0.000 -7.84e-05 -2.93e-05\n",
"num_critic_for_reviews 0.0026 0.000 13.581 0.000 0.002 0.003\n",
"num_user_for_reviews -0.0006 5.56e-05 -10.546 0.000 -0.001 -0.000\n",
"num_voted_users 3.357e-06 1.65e-07 20.288 0.000 3.03e-06 3.68e-06\n",
"facenumber_in_poster -0.0253 0.007 -3.853 0.000 -0.038 -0.012\n",
"==============================================================================\n",
"Omnibus: 601.448 Durbin-Watson: 1.975\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 1317.164\n",
"Skew: -0.927 Prob(JB): 9.58e-287\n",
"Kurtosis: 5.213 Cond. No. 6.77e+15\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The smallest eigenvalue is 9.34e-13. This might indicate that there are\n",
"strong multicollinearity problems or that the design matrix is singular.\n"
]
}
],
"source": [
"## Build regression model based on ordinary least squares\n",
"runs_reg_model = ols(\"imdb_score~title_year+content_rating+duration+from_USA+in_english+color+gross+budget+profit+movie_facebook_likes+actor_1_facebook_likes+actor_2_facebook_likes+actor_3_facebook_likes+cast_total_facebook_likes+num_critic_for_reviews+num_user_for_reviews+num_voted_users+facenumber_in_poster\", df1)\n",
"runs_reg = runs_reg_model.fit()\n",
"print(runs_reg.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The R-squared terms of Statsmodel and Scikit-learn models are virtually identical (0.416), but Statsmodel has a slightly higher MSE, meaning it's a slightly less accurate model than Scikit-learn."
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.6525603200981047"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Evaluation of MSE\n",
"runs_reg.mse_resid"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### c. Regularization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll build a regression model using Lasso regularization algorithm. "
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"## Build regression model based on regularization algorithm\n",
"modelL = lm.Lasso()\n",
"modelL.fit(X, y)\n",
"modelL_y = modelL.predict(X)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('title_year', '-0.004'),\n",
" ('content_rating', '0.000'),\n",
" ('duration', '0.011'),\n",
" ('from_USA', '-0.000'),\n",
" ('in_english', '-0.000'),\n",
" ('color', '-0.000'),\n",
" ('gross', '-0.000'),\n",
" ('budget', '-0.000'),\n",
" ('profit', '0.000'),\n",
" ('movie_facebook_likes', '-0.000'),\n",
" ('director_facebook_likes', '0.000'),\n",
" ('actor_1_facebook_likes', '0.000'),\n",
" ('actor_2_facebook_likes', '0.000'),\n",
" ('actor_3_facebook_likes', '0.000'),\n",
" ('cast_total_facebook_likes', '-0.000'),\n",
" ('num_critic_for_reviews', '0.002'),\n",
" ('num_user_for_reviews', '-0.000'),\n",
" ('num_voted_users', '0.000'),\n",
" ('facenumber_in_poster', '-0.000')]"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Display coefficients for each variable\n",
"coef = [\"%.3f\" % i for i in modelL.coef_]\n",
"xcolumns = [ i for i in X.columns ]\n",
"list(zip(xcolumns, coef))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This model has a higher MSE and a lower R-squared than previous models, meaning it's less accurate."
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean Square Error: 0.705537813993835\n",
"Variance or R-squared: 0.36585279718498065\n"
]
}
],
"source": [
"## Model evaluation\n",
"print(\"Mean Square Error: \", mean_squared_error(y, modelL_y))\n",
"print(\"Variance or R-squared: \", explained_variance_score(y, modelL_y))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### d. Feature selection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we'll build a model using feature selection. SelectKBest will choose the 12 most important variables that we'll be able to build a regression model from."
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0 1 2 3 4 6 8 9 10 15 16 17]\n"
]
}
],
"source": [
"## Select 12 most important variables using SelectKBest algorithm\n",
"X1 = SelectKBest(f_regression, k=12).fit_transform(X, y)\n",
"selector = SelectKBest(f_regression, k=12).fit(X, y)\n",
"idxs_selected = selector.get_support(indices=True)\n",
"print(idxs_selected)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
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"\n",
"
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title_year
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content_rating
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"
duration
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from_USA
\n",
"
in_english
\n",
"
color
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"
gross
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"
budget
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"
profit
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"
movie_facebook_likes
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"
director_facebook_likes
\n",
"
actor_1_facebook_likes
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"
actor_2_facebook_likes
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"
actor_3_facebook_likes
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"
cast_total_facebook_likes
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"
num_critic_for_reviews
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num_user_for_reviews
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num_voted_users
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facenumber_in_poster
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2009.0
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"text/plain": [
" title_year content_rating duration from_USA in_english color \\\n",
"0 2009.0 3 178.0 1.0 1.0 1 \n",
"\n",
" gross budget profit movie_facebook_likes \\\n",
"0 760505847.0 237000000.0 523505847.0 33000 \n",
"\n",
" director_facebook_likes actor_1_facebook_likes actor_2_facebook_likes \\\n",
"0 0.0 1000.0 936.0 \n",
"\n",
" actor_3_facebook_likes cast_total_facebook_likes num_critic_for_reviews \\\n",
"0 855.0 4834 723.0 \n",
"\n",
" num_user_for_reviews num_voted_users facenumber_in_poster \n",
"0 3054.0 886204 0.0 "
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Check which variables were chosen\n",
"X.head(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The 12 most important variables according to SelectKBest are:\n",
"* title year\n",
"* content rating\n",
"* duration\n",
"* from USA\n",
"* in english\n",
"* gross\n",
"* profit\n",
"* movie facebook likes\n",
"* director facebook likes\n",
"* num critic for reviews\n",
"* num user for reviews\n",
"* num voted users\n",
"\n",
"The regression model built with feature selection has a higher MSE and a lower R-squared than previously tested models meaning it's less accurate."
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean Square Error: 0.6608544967460929\n",
"Variance or R-squared: 0.4060147843713958\n"
]
}
],
"source": [
"## Build regression model based on 12 chosen variables\n",
"modelF = lm.LinearRegression()\n",
"modelF.fit(X1, y)\n",
"modelF_y = modelF.predict(X1)\n",
"## Model evaluation\n",
"print(\"Mean Square Error: \", mean_squared_error(y, modelF_y))\n",
"print(\"Variance or R-squared: \", explained_variance_score(y, modelF_y))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### e. Testing best model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By analyzing the MSE of each of the regression models, we can tell that Scikit-learn is the most accurate model because it has the lowest MSE of the 4 models tested. This means that the Scikit-learn model has reduced the total amount of error in its regression model."
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Scikit-learn MSE: 0.6492138737974602\n",
"Statsmodel MSE: 0.6525603200981047\n",
"Regularization MSE: 0.705537813993835\n",
"Feature selection MSE: 0.6608544967460929\n"
]
}
],
"source": [
"## Evaluation of all models\n",
"print(\"Scikit-learn MSE:\", mean_squared_error(y, model1_y))\n",
"print(\"Statsmodel MSE:\", runs_reg.mse_resid)\n",
"print(\"Regularization MSE:\", mean_squared_error(y, modelL_y))\n",
"print(\"Feature selection MSE:\", mean_squared_error(y, modelF_y))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The plot below shows the actual IMDb scores plotted against Scikit-learn's predicted scores. Below the plot is the root mean square (RMS) of the Scikit-learn model. This is helpful in analysis because RMS puts the error term into the same scale as the y variable. In other words, on average, this model wrongly predicts the IMDb score by 0.8. This seems very high, especially since 0.8 can be the difference between a good movie and a great movie. However, the reason for the high RMS is that the model doesn't predict IMDb scores below 5 or 6. This means that there is a greater amount of error in lower IMDb rated movies, which increases the error overall. This is most likely due to the nature of the dataset and not an issue with the model. In other words, because of the dataset, the model will be more accurate with IMDb scores above 5 or 6."
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Scikit-learn RMS: 0.8057380925570419\n"
]
}
],
"source": [
"## Scatterplot actual vs predicted for Scikit-learn\n",
"plt.subplots()\n",
"plt.scatter(y, model1_y)\n",
"plt.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4) #dotted line represents perfect prediction (actual = predicted)\n",
"plt.title('Scikit-learn Actual vs Predicted')\n",
"plt.xlabel('Actual IMDb Score')\n",
"plt.ylabel('Predicted IMDb Score')\n",
"plt.show()\n",
"## Display RMS of Scikit-learn model\n",
"print(\"Scikit-learn RMS: \", sqrt(mean_squared_error(y, model1_y)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Classification"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Earlier on in the project, we set custom bins based on the IMDb score of the movie. \n",
"* Movies in bin 1 represent 'bad' movies (0-4 IMDb score)\n",
"* Movies in bin 2 represent 'ok' movies (4-6 IMDb score)\n",
"* Movies in bin 3 represent 'good' movies (6-8 IMDb score)\n",
"* Movies in bin 4 movies represent 'excellent' movies (8-10 IMDb score)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1 95\n",
"2 1067\n",
"3 2476\n",
"4 156\n",
"Name: movie_quality, dtype: int64"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Show custom movie quality bins\n",
"df1['movie_quality'].value_counts().sort_index()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can use the dataset to build classification models to predict which bin movies will classify as. Effectively we are predicting whether a movie is 'bad,' 'ok,' 'good,' or 'excellent' instead of predicting the IMDb score overall."
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(3794,) (3794, 19)\n"
]
}
],
"source": [
"## Declare X/y variables\n",
"\n",
"y = df1['movie_quality']\n",
"X = df1.drop(['movie_quality', 'imdb_score'], axis =1)\n",
"\n",
"print(y.shape, X.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### a. Decision tree"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, we'll use a decision tree classification model. This model splits the dataset into training and testing groups. 70% of the data will be used to train the algorithm, while the remaining 30% will be used to test the model and its accuracy."
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7155399473222125\n"
]
}
],
"source": [
"## Validation\n",
"X_train, X_test_dt, y_train, y_test_dt = train_test_split(X, y, test_size=0.3, random_state=0)\n",
"## Initialize\n",
"dt = DecisionTreeClassifier(max_depth=5, min_samples_leaf=5)\n",
"## Train\n",
"dt = dt.fit(X_train, y_train)\n",
"## Model evaluation\n",
"print(metrics.accuracy_score(y_test_dt, dt.predict(X_test_dt)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A classification model is evaluated by its accuracy, the amount of correctly classified data points in the test set divided by the total number of data points in the test set. In this case, the model correctly classifies the data 71.5% of the time. A confusion matrix is a visualization of the accuracy of a classification model. The goal of a confusion matrix is to maximize the true rates, the numbers diagonal from top left to bottom right. Naturally, when maximizing the true rates we'll want to minimize the rest, which are called false rates. In this case, the true rate is high enough to suggest a decent amount of accuracy in the model. However, it's worth noting that the model does not correctly predict any 'bad' movies and instead falsely classifies them as 'ok' or 'good.' Similarly, there are quite a few instances of 'ok' movies that are misclassified as 'good' movies. This is a similar phenomenon found in the regression model in which the model does not predict IMDb scores under 5. This further proves the theory that this error is due to the nature of the dataset and not the algorithm itself."
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"## Confusion matrix\n",
"skplt.metrics.plot_confusion_matrix(y_true=np.array(y_test_dt), y_pred=dt.predict(X_test_dt))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Shown below is the output of the decision tree used to classify each movie. The decision tree starts with num_voted_users, splits them into two groups, above and below 89,788, then moves to the next node. This process repeats until all observations have been classified. The gini score represents how homogeneous the data in each node is. Zero represents complete homogeneousness, which is the goal of classification. The issue with this decision tree is that some of the nodes on the final level are mostly heterogeneous when they should be completely homogeneous in an ideal model. Again, this is most likely due to the nature of the dataset where the algorithms tend to misclassify movies that have a lower IMDb score."
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Display decision tree\n",
"Source(tree.export_graphviz(dt, out_file=None, feature_names=X.columns))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### b. KNN"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we'll use KNN to build a classification model. Again, like the decision tree, the data is split into 70% training and 30% testing. KNN stands for k-nearest neighbor. The algorithm uses the training data to search for the nearest neighbor of a test data point and classifies it accordingly."
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5943810359964882\n"
]
}
],
"source": [
"## Validation\n",
"X_train, X_test_knn, y_train, y_test_knn = train_test_split(X, y, test_size=0.3, random_state=0)\n",
"## Initialize\n",
"knn = KNeighborsClassifier()\n",
"## Train\n",
"knn = knn.fit(X_train, y_train)\n",
"## Model evaluation\n",
"print(metrics.accuracy_score(y_test_knn, knn.predict(X_test_knn)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The accuracy of the KNN model is shown above. The model correctly classifies the data 59.4% of the time. This is substantially lower than the decision tree model. According to the confusion matrix below, it appears that most of the error is coming from 'ok' movies misclassified as 'good' movies."
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"## Confusion matrix\n",
"skplt.metrics.plot_confusion_matrix(y_true=np.array(y_test_knn), y_pred=knn.predict(X_test_knn))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### c. Logistic regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Logistic regression is similar to linear regression used earlier in the regression section. However, logistic regression is used for classification and is a non-linear model. Again we'll split the data into 70% training and 30% testing."
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.659350307287094\n"
]
}
],
"source": [
"## Validation\n",
"X_train, X_test_lr, y_train, y_test_lr = train_test_split(X, y, test_size=0.3, random_state=0)\n",
"## Initialize\n",
"lr = LogisticRegression(solver='lbfgs', max_iter=500)\n",
"## Train\n",
"lr.fit(X_train, y_train)\n",
"## Model evaluation\n",
"print(metrics.accuracy_score(y_test_lr, lr.predict(X_test_lr)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The accuracy of the logistic regression model is shown above. The model correctly classifies the data 65.9% of the time. This is more accurate than the KNN model but still less accurate than the decision tree model. The confusion matrix shown below reveals that the algorithm mostly predicts movies as 'good' because that's where the bulk of the data is. This is not conducive to a good classification model."
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"## Confusion matrix\n",
"skplt.metrics.plot_confusion_matrix(y_true=np.array(y_test_lr), y_pred=lr.predict(X_test_lr))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### d. Random forest classifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll build a random forest classification model. Random forest operates similarly to the decision tree model, but instead of building one decision tree, it builds 20 decision trees. Again we'll split the data into 70% training and 30% testing."
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7594381035996488\n"
]
}
],
"source": [
"## Validation\n",
"X_train, X_test_clf, y_train, y_test_clf = train_test_split(X, y, test_size=0.3, random_state=0)\n",
"## Initialize\n",
"clf = RandomForestClassifier(n_estimators=20)\n",
"## Train\n",
"clf=clf.fit(X_train, y_train)\n",
"## Model evaluation\n",
"print(metrics.accuracy_score(y_test_clf, clf.predict(X_test_clf)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Random forest is the most accurate of any model tested so far. The model correctly classifies the data 76% of the time. The confusion matrix above shows that the random forest model is not only more accurate but also doesn't rely on classifying the most commonly occurred class for its high accuracy."
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"## Confusion matrix\n",
"skplt.metrics.plot_confusion_matrix(y_true=np.array(y_test_clf), y_pred=clf.predict(X_test_clf))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### f. Recursive feature selection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recursive feature selection ranks the importance of each variable. Those variables are then used to build a logistic regression classification model."
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [],
"source": [
"## Rank importance of variables based on feature selection\n",
"model = LogisticRegression()\n",
"rfe = RFE(model, 1)\n",
"rfe = rfe.fit(X, y)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
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"\n",
"
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" \n",
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variable
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importance
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" \n",
" \n",
"
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0
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in_english
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1
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1
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color
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2
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2
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from_USA
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3
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3
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content_rating
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4
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4
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facenumber_in_poster
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5
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5
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duration
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6
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6
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num_critic_for_reviews
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7
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7
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title_year
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8
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8
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num_user_for_reviews
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9
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9
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actor_2_facebook_likes
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10
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"text/plain": [
" variable importance\n",
"0 in_english 1\n",
"1 color 2\n",
"2 from_USA 3\n",
"3 content_rating 4\n",
"4 facenumber_in_poster 5\n",
"5 duration 6\n",
"6 num_critic_for_reviews 7\n",
"7 title_year 8\n",
"8 num_user_for_reviews 9\n",
"9 actor_2_facebook_likes 10"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Features sorted by their rank\n",
"pd.DataFrame({'variable':X.columns, 'importance':rfe.ranking_}).sort_values(by='importance').reset_index().drop('index', axis=1).head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll use the top 10 most important variables according to RFE shown above to build a logistic regression model."
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.6646180860403863\n"
]
}
],
"source": [
"## Select top 10 ranked variables from RFE\n",
"X_logistic = df[['in_english','color','from_USA', 'content_rating', 'facenumber_in_poster', 'duration', 'num_critic_for_reviews', 'title_year', 'num_user_for_reviews', 'actor_2_facebook_likes']]\n",
"## Validation\n",
"X_train, X_test_lr1, y_train, y_test_lr1 = train_test_split(X_logistic, y, test_size=0.3, random_state=0)\n",
"## Initialize\n",
"lr1 = LogisticRegression()\n",
"## Train\n",
"lr1.fit(X_train, y_train)\n",
"#Model evaluation\n",
"print(metrics.accuracy_score(y_test_lr1, lr1.predict(X_test_lr1)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The RFE model correctly classifies the data 66.3% of the time. Because RFE uses logistic regression, most movies are classified as 'good' movies because that's where most of the training data is classified. This is not conducive to building a good classification model."
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"## Confusion matrix\n",
"skplt.metrics.plot_confusion_matrix(y_test_lr1, lr1.predict(X_test_lr1))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### g. Best model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The random forest model is the most accurate of all models tested, both in its overall accuracy and in the visualization of the confusion matrix. The random forest model correctly classifies the data 76% of the time."
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Decision tree accuracy: 0.7155399473222125\n",
"KNN accuracy: 0.5943810359964882\n",
"Logistic regression accuracy: 0.659350307287094\n",
"Random forest classifier accuracy: 0.7594381035996488\n",
"Recursive feature selection accuracy: 0.6646180860403863\n"
]
}
],
"source": [
"## Print accuracy score for each model\n",
"dt = metrics.accuracy_score(y_test_dt, dt.predict(X_test_dt))\n",
"knn = metrics.accuracy_score(y_test_knn, knn.predict(X_test_knn))\n",
"lr = metrics.accuracy_score(y_test_lr, lr.predict(X_test_lr))\n",
"clf = metrics.accuracy_score(y_test_clf, clf.predict(X_test_clf))\n",
"lr1 = metrics.accuracy_score(y_test_lr1, lr1.predict(X_test_lr1))\n",
"print('Decision tree accuracy: %s' % (dt))\n",
"print('KNN accuracy: %s' % (knn))\n",
"print('Logistic regression accuracy: %s' % (lr))\n",
"print('Random forest classifier accuracy: %s' % (clf))\n",
"print('Recursive feature selection accuracy: %s' % (lr1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Clustering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The goal of clustering is to segment the data into distinct clusters and develop cluster profiles based on each cluster's information. The first step is to determine the most important variables to use in the clustering analysis. We'll select the 8 most important variables according to feature importance."
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
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importance
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" \n",
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num_voted_users
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0.115421
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duration
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0.073528
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num_critic_for_reviews
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0.069830
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budget
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0.066979
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"text/plain": [
" importance\n",
"num_voted_users 0.115421\n",
"duration 0.073528\n",
"num_critic_for_reviews 0.069830\n",
"budget 0.066979\n",
"title_year 0.066449\n",
"num_user_for_reviews 0.064334\n",
"profit 0.060909\n",
"gross 0.059518"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Select 8 most important variables with feature importance\n",
"model_extra = ExtraTreesClassifier()\n",
"model_extra.fit(X, y)\n",
"pd.DataFrame(model_extra.feature_importances_, index = X.columns, columns=['importance']).sort_values('importance', ascending=False).head(8)"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [],
"source": [
"## New dataframe based on these variables\n",
"df_c = df1[['title_year', 'duration', 'gross', 'budget', 'profit', 'num_critic_for_reviews', 'num_user_for_reviews', 'num_voted_users']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### a. Normalize data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll need to normalize the data before performing cluster analysis. The reason for this is that the scale or variance of each variable is unique and will distort the importance of the clusters. For example, the variance of gross at 4.8 quadrillions, is much higher than the title year variance at 99.2, and the algorithm will give more importance to the higher variance. The point of normalization is to make sure all variables are on the same scale, and therefore on a level playing field when determining the importance of each variable."
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"title_year 99.151\n",
"duration 500.154\n",
"gross 4,841,046,710,277,035.000\n",
"budget 1,851,932,001,982,648.750\n",
"profit 2,812,299,093,271,696.500\n",
"num_critic_for_reviews 15,337.725\n",
"num_user_for_reviews 167,781.108\n",
"num_voted_users 22,803,705,819.598\n",
"dtype: float64"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Check variance\n",
"pd.options.display.float_format = '{:,.3f}'.format\n",
"df_c.var()"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": [
"## Reset scientific notation\n",
"pd.reset_option('^display.', silent=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that the data has been normalized, all variables share the same scale, and the size of the initial variance won't influence the clustering results."
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"title_year 0.012518\n",
"duration 0.005826\n",
"gross 0.008370\n",
"budget 0.020577\n",
"profit 0.004148\n",
"num_critic_for_reviews 0.023262\n",
"num_user_for_reviews 0.006556\n",
"num_voted_users 0.007987\n",
"dtype: float64"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Normalize data and check variance again\n",
"df_norm = (df_c - df_c.mean()) / (df_c.max() - df_c.min())\n",
"df_norm.var()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### b. Elbow method for determining number of clusters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The elbow plot below helps determine the optimal number of clusters for a dataset. Too few clusters and there will be a loss of individual information, too many clusters and analysis will be more difficult. The elbow plot's namesake comes from the optimal cluster point resembling an elbow joint. It appears that the 'elbow' is at 3 clusters. This analysis is subjective though and others may argue 2 or 4 clusters."
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
],
"text/plain": [
" title_year duration gross budget profit \\\n",
"0 2009.0 178.0 760505847.0 237000000.0 523505847.0 \n",
"\n",
" num_critic_for_reviews num_user_for_reviews num_voted_users cluster \n",
"0 723.0 3054.0 886204 0.0 "
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Cluster with original data\n",
"df_c3 = df_c.join(df_c1)\n",
"df_c3.head(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The number of movies in each cluster is shown below. The majority of observations fit into cluster 2 (with the index 1), while only a select few have made it into cluster 1 (with the index 0). *Note: python indexing starts at 0 not 1*"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0 271\n",
"1.0 1854\n",
"2.0 1106\n",
"Name: cluster, dtype: int64"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Number of observations in each cluster\n",
"df_c3['cluster'].value_counts().sort_index()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can view each cluster's average values for each variable selected. Before we analyze this information and develop profiles from it, we need to make sure certain cluster values are significantly different from other clusters with more certainty than just an arbitrary guess."
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
cluster
\n",
"
0.0
\n",
"
1.0
\n",
"
2.0
\n",
"
\n",
" \n",
" \n",
"
\n",
"
title_year
\n",
"
2,008.092
\n",
"
2,003.050
\n",
"
2,003.280
\n",
"
\n",
"
\n",
"
duration
\n",
"
123.993
\n",
"
109.019
\n",
"
112.955
\n",
"
\n",
"
\n",
"
gross
\n",
"
158,429,687.048
\n",
"
41,913,748.528
\n",
"
61,262,531.428
\n",
"
\n",
"
\n",
"
budget
\n",
"
136,912,915.129
\n",
"
28,233,330.099
\n",
"
46,975,311.394
\n",
"
\n",
"
\n",
"
profit
\n",
"
21,516,771.919
\n",
"
13,680,418.428
\n",
"
14,287,220.033
\n",
"
\n",
"
\n",
"
num_critic_for_reviews
\n",
"
308.886
\n",
"
152.780
\n",
"
172.290
\n",
"
\n",
"
\n",
"
num_user_for_reviews
\n",
"
744.550
\n",
"
291.023
\n",
"
357.489
\n",
"
\n",
"
\n",
"
num_voted_users
\n",
"
250,640.672
\n",
"
91,945.350
\n",
"
115,319.672
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"cluster 0.000 1.000 2.000\n",
"title_year 2,008.092 2,003.050 2,003.280\n",
"duration 123.993 109.019 112.955\n",
"gross 158,429,687.048 41,913,748.528 61,262,531.428\n",
"budget 136,912,915.129 28,233,330.099 46,975,311.394\n",
"profit 21,516,771.919 13,680,418.428 14,287,220.033\n",
"num_critic_for_reviews 308.886 152.780 172.290\n",
"num_user_for_reviews 744.550 291.023 357.489\n",
"num_voted_users 250,640.672 91,945.350 115,319.672"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Mean values for each cluster\n",
"pd.options.display.float_format = '{:,.3f}'.format\n",
"df_c3.groupby('cluster').mean().T"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [],
"source": [
"## Reset scientific notation\n",
"pd.reset_option('^display.', silent=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### d. t-Testing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The purpose of t-testing is to find statistically significant differences between two numbers. In this case, for each variable, we'll test whether each of the 3 cluster's values deviates significantly from other clusters."
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
"## Develop t-test for each variable between each cluster\n",
"a = pg.pairwise_tukey(data=df_c3, dv='title_year', between='cluster')['p-tukey']\n",
"b = pg.pairwise_tukey(data=df_c3, dv='duration', between='cluster')['p-tukey'] \n",
"c = pg.pairwise_tukey(data=df_c3, dv='gross', between='cluster')['p-tukey']\n",
"d = pg.pairwise_tukey(data=df_c3, dv='budget', between='cluster')['p-tukey'] \n",
"e = pg.pairwise_tukey(data=df_c3, dv='profit', between='cluster')['p-tukey'] \n",
"f = pg.pairwise_tukey(data=df_c3, dv='num_critic_for_reviews', between='cluster')['p-tukey'] \n",
"g = pg.pairwise_tukey(data=df_c3, dv='num_user_for_reviews', between='cluster')['p-tukey']\n",
"h = pg.pairwise_tukey(data=df_c3, dv='num_voted_users', between='cluster')['p-tukey']"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [],
"source": [
"## Define highlight function\n",
"def color(val):\n",
" color = 'red' if val > 0.05 else ''\n",
" return 'background-color: %s' % color"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Two numbers are statistically significantly different if the p-value is less than 0.05. The cells highlighted in red have a p-value above this cutoff value, therefore they are not statistically significant. So in terms of title year, cluster 1 is statistically different than cluster 2 and the same is true between clusters 1 and 3. However, title year is not significantly different between clusters 2 and 3. Profit is the only variable in which none of the clusters are statistically significant because the p-values are greater than 0.05. Therefore we will leave these highlighted cells out of our cluster profiles. The rest of the variables are statistically significant between each of the clusters. Now that we have this information, we can develop cluster profiles."
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
year
duration
gross
budget
profit
num_critic_for_reviews
num_user_for_reviews
num_voted_users
difference between clusters
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"
\n",
"
1 and 2
\n",
"
0.001
\n",
"
0.001
\n",
"
0.001
\n",
"
0.001
\n",
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0.0827
\n",
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0.001
\n",
"
0.001
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0.001
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\n",
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\n",
"
1 and 3
\n",
"
0.001
\n",
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0.001
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0.001
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0.001
\n",
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0.141
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0.001
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0.001
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0.001
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"
\n",
"
\n",
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2 and 3
\n",
"
0.818
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0.001
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"
0.001
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0.001
\n",
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0.9
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0.001
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0.001
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0.001
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"
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],
"text/plain": [
""
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Convert p-values to dataframe\n",
"ttest = pd.concat([a, b, c, d, e, f, g, h], axis=1)\n",
"ttest.columns = ['year', 'duration', 'gross', 'budget', 'profit', 'num_critic_for_reviews', 'num_user_for_reviews', 'num_voted_users']\n",
"ttest.index.name = 'difference between clusters'\n",
"ttest = ttest.rename(index={0: '1 and 2', 1: '1 and 3', 2: '2 and 3'})\n",
"ttest.style.applymap(color).format(\"{:.3}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
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"### e. Cluster profiles"
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"__Cluster 1 - \"Blockbusters\"__ _These are the movies that are a hit at the box office, everyone's talking about them, and are in contention during awards season._ Compared to other clusters these movies on average are:\n",
"* More recently released\n",
"* Longer in duration\n",
"* Very high grossing with very high budget\n",
"* Very high number of critic reviews, user reviews, and voted users\n",
"\n",
"__Cluster 2 - \"Indie Movies/Cult Classics\"__ _These are the movies that are made with limited resources and don't make large amounts at the box office but still have their place in the cinema world._ Compared to other clusters these movies on average are:\n",
"* Shorter in duration\n",
"* Lower grossing movies with lower budget\n",
"* Lower number of critic reviews, user reviews, and voted users\n",
"\n",
"__Cluster 3 - \"Rainy Day Films\"__ _These movies bridge the gap between the extremes of blockbusters and indie movies/cult classics. They have a decent sized budget and usually recoup their budget at the box office, but don't garner the same amount of attention or award nominations._ Compared to other clusters these movies on average are:\n",
"* Right in the middle in terms of duration\n",
"* Average budget and gross\n",
"* Between the other clusters in terms of critic reviews, user reviews, and voted users"
]
},
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"source": [
"## 8. Storytelling and Conclusion"
]
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"source": [
"### a. Limitations"
]
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"Before concluding the analysis, it's worth noting that there are a few drawbacks to this dataset and project:\n",
"* The dataset features outdated and inaccurate information\n",
" * The most recent movies in the dataset were released in 2016\n",
" * IMDb scores, number of reviews and votes, change daily and are therefore outdated\n",
" * Facebook likes were scraped around this time also and therefore are outdated\n",
" * It's unclear whether or not financial information like budget and gross were adjusted for inflation and if gross is purely box office sales or incorporates streaming\n",
" * Some outliers were removed, like a movie with a 12 billion dollar budget, but other, less obvious outliers may still exist putting the data integrity into question\n",
"* The dataset uses some variables that are useless for predicting the success of a movie before release\n",
" * Some of the most important variables in the dataset involve the number of IMDb reviews a movie has which is impossible to tell before a movie is released\n",
"* The dataset fails to predict movies below a certain IMDb rating threshold\n",
" * There is a strange phenomenon within this dataset where each algorithm, whether regression, classification, or clustering, fails to predict movies below an IMDb rating of about 5-6\n",
" * This is very apparent in section 5e where the actual versus predicted values are plotted and no values are predicted below an IMDb score of 4.5\n",
" * This means that each model will be more accurate with movies above an IMDb score of 5-6 and much less accurate below that threshold"
]
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"source": [
"### b. Data Visualization & Correlation"
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"According to the dataset and analysis, movies are more likely to have these features:\n",
"* Made from the late 90's onward\n",
"* Drama or Comedy\n",
"* Content rating of PG-13 or R\n",
"* Runtime around 100 minutes (1 hour and 40 minutes)\n",
"* Gross less than 10 million dollars\n",
"* Budget of less than 15 million dollars\n",
"* Just breakeven in terms of profit\n",
"* Less than 150 reviews from critics on IMDb\n",
"* Less than 200 user reviews on IMDb\n",
"* Less than 20,000 user votes on IMDb\n",
"\n",
"In general, movies with a **higher** IMDb score are more likely to have these features:\n",
"* Longer runtime/duration\n",
"* More 'adult' content rating\n",
"* Released further in the past\n",
"* Black and white\n",
"* Made in a country other than the United States\n",
"* In a language other than English\n",
"* Less faces in the movie poster\n",
"* Higher budget, box office gross, and overall profit\n",
"* Director or actor with a higher than average IMDb score\n",
"* More Facebook likes on the movie's, director's, and cast's Facebook pages\n",
"* More user votes, reviews from critics, and user reviews on IMDb"
]
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"source": [
"### c. Regression"
]
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"The goal of regression was to predict IMDb score, the y variable, using various numerical determinants from the dataset. We learned the following things through regression:\n",
"* The strength or accuracy of a regression model is evaluated through mean square error (MSE) and R-squared. The goal of a regression model is to minimize MSE and maximize R-squared. A lower MSE means less error in the model.\n",
"* The most accurate regression model tested was Scikit-learn because it had the lowest MSE at 0.649 and therefore reduced the total amount of error in the model.\n",
"* The RMS or root mean square of the regression model puts the error term into the same scale as the y variable. On average, the Scikit-learn model wrongly predicts the IMDb score by 0.8 points. This seems very high, especially since 0.8 can be the difference between a good movie and a great movie, but this error is most likely due to the limitations of the dataset previously discussed."
]
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"source": [
"### d. Classification"
]
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{
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"The goal of classification was to predict whether a movie is 'bad,' 'ok,' 'good,' or 'excellent' instead of predicting the IMDb score overall. We achieved this by creating custom bins based on IMDb score. These are the things we learned through classification:\n",
"* All models split the dataset into training and testing. 70% of the data was used to train the algorithm, while the remaining 30% was used to test the model and its accuracy.\n",
"* A classification model is evaluated by its accuracy, the amount of correctly classified data points in the test set divided by the total number of data points in the test set.\n",
"* Confusion matrices visualize the accuracy of a classification model and the goal is to maximize the true rates and minimize the false rates.\n",
"* The random forest model was the most accurate of all models tested, both in its overall accuracy and in the visualization of the confusion matrix. The majority of movies fit into the 'good' bin, and the model did not classify most or all of the data in this bin for the sake of total accuracy. The random forest model correctly classified the data 76% of the time."
]
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"metadata": {},
"source": [
"### e. Clustering"
]
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"source": [
"The goal of clustering was to segment the data into distinct clusters and develop profiles based on each cluster's information using the 8 most important x variables. These are the things we learned after clustering:\n",
"* We had to normalize the data before clustering so that variables were all on the same scale in terms of variance. Without normalization, the variables with a higher variance would be given more weight and distort the importance when determining clusters. \n",
"* The ‘elbow’ in the elbow plot was at 3 clusters, therefore a total of 3 clusters were used. However, this is a subjective analysis, and others may have chosen a different number of clusters.\n",
"* t-Testing was used to find statistically significant differences between each of the 3 clusters for each variable. Two clusters are statistically significantly different if the p-value is less than 0.05.\n",
"* Using information gleaned from t-testing, we were able to build profiles with statistical certainty that each variable discussed was significantly different compared to other clusters. These profiles were:\n",
" * __Cluster 1 - \"Blockbusters\"__ Compared to other clusters these movies on average are:\n",
" * More recently released\n",
" * Longer in duration\n",
" * Very high grossing with very high budget\n",
" * Very high number of critic reviews, user reviews, and voted users\n",
"\n",
" * __Cluster 2 - \"Indie Movies/Cult Classics\"__ Compared to other clusters these movies on average are:\n",
" * Shorter in duration\n",
" * Lower grossing movies with lower budget\n",
" * Lower number of critic reviews, user reviews, and voted users\n",
"\n",
" * __Cluster 3 - \"Rainy Day Films\"__ Compared to other clusters these movies on average are:\n",
" * Right in the middle in terms of duration\n",
" * Average budget and gross\n",
" * Between the other clusters in terms of critic reviews, user reviews, and voted users"
]
},
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"metadata": {},
"source": [
"### f. Implications"
]
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"This dataset can be given to an executive producer, actor, director, agent, or anyone else in the movie business to decide if a potential movie is worth taking on. Not necessarily in terms of making money but in terms of making a quality movie and winning awards. Before taking a role, producers, actors, directors, and others in the movie business should ask themselves:\n",
"* How long is the proposed runtime/duration?\n",
"* What is the proposed content rating for this movie?\n",
"* Will this movie be in color or black and white?\n",
"* What country will this movie be made in?\n",
"* What language will this country be in?\n",
"* What is the proposed budget for this movie?\n",
"* Will this movie feature a director or actor with a higher than average IMDb score?\n",
"* How many Facebook likes do the director and actor's pages have?"
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