{ "cells": [ { "cell_type": "markdown", "metadata": { "_uuid": "ece6e836e0e2edb8138c98cb1d67d092c932ed1b" }, "source": [ "![Pet Banner](Images/header.png)\n", "#
Predicting Pet Adoption Time Using Decision Tree Ensembles
\n" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "2f975daaf15bb14f2584e8453016fea261c233de" }, "source": [ "
\n", "The goal of this project is to develop an algorithm to predict the adoptability of pets - specifically, how quickly is a pet adopted? The dataset was part of the [PetFinder.my Adoption Prediction](https://www.kaggle.com/c/petfinder-adoption-prediction).\n", "\n", "The final ensemble model used four different classifier algorithms (**Gradient Boosting**, **AdaBoost**, **Random Forest**, and **XGBoost**). The final prediction classes were determined as the average of all four models weighted equally.\n", "\n", "
\n", "\n", "**Table of Contents**\n", "1. [Data Columns](#Data-Columns)\n", "2. [Dataset Overview](#Data-Overview)\n", "3. [Missing Data](#Missing-Data)\n", "4. [Variable Visualizations](#Variable-Visualizations)\n", "5. [Caution Variables](#Caution-Variables)\n", "6. [Data Cleaning](#Data-Cleaning)\n", "7. [Tree Ensemble Modelling](#Tree-Ensemble-Modelling)\n", "8. [Predictions & Submission](#Predictions-&-Submission)\n", "\n", "
\n", "\n", "**Model Accuracy**: (Cohen's Kappa = 0.33809)\n", "\n", " Top submissions on the public leaderboard are around 0.45\n", " \n", "\n", "**Competition Description**\n", "> Millions of stray animals suffer on the streets or are euthanized in shelters every day around the world. If homes can be found for them, many precious lives can be saved — and more happy families created.\n", "\n", "\n", "> PetFinder.my has been Malaysia’s leading animal welfare platform since 2008, with a database of more than 150,000 animals. PetFinder collaborates closely with animal lovers, media, corporations, and global organizations to improve animal welfare.\n", "\n", "> Animal adoption rates are strongly correlated to the metadata associated with their online profiles, such as descriptive text and photo characteristics. As one example, PetFinder is currently experimenting with a simple AI tool called the Cuteness Meter, which ranks how cute a pet is based on qualities present in their photos.\n", "\n", "> In this competition you will be developing algorithms to predict the adoptability of pets - specifically, how quickly is a pet adopted? If successful, they will be adapted into AI tools that will guide shelters and rescuers around the world on improving their pet profiles' appeal, reducing animal suffering and euthanization.\n", "\n", "> Top participants may be invited to collaborate on implementing their solutions into AI tools for assessing and improving pet adoption performance, which will benefit global animal welfare.\n", "\n", "**Competition Evaluation**\n", "> Submissions are scored based on the quadratic weighted kappa, which measures the agreement between two ratings. This metric typically varies from 0 (random agreement between raters) to 1 (complete agreement between raters). \n", "\n", "> In the event that there is less agreement between the raters than expected by chance, the metric may go below 0. The quadratic weighted kappa is calculated between the scores which are expected/known and the predicted scores.\n" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2019-04-18T15:54:41.751103Z", "start_time": "2019-04-18T15:54:41.743106Z" } }, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "718e7883400e71516ff672c23f29724b2fe35c0a" }, "source": [ "## Data Columns\n", "[Source](https://www.kaggle.com/c/petfinder-adoption-prediction/data)\n", "\n", "* **PetID** - Unique hash ID of pet profile\n", "* **AdoptionSpeed** - Categorical speed of adoption. Lower is faster. This is the value to predict. See below section for more info.\n", "* **Type** - Type of animal (1 = Dog, 2 = Cat)\n", "* **Name** - Name of pet (Empty if not named)\n", "* **Age** - Age of pet when listed, in months\n", "* **Breed1** - Primary breed of pet (Refer to BreedLabels dictionary)\n", "* **Breed2** - Secondary breed of pet, if pet is of mixed breed (Refer to BreedLabels dictionary)\n", "* **Gender** - Gender of pet (1 = Male, 2 = Female, 3 = Mixed, if profile represents group of pets)\n", "* **Color1** - Color 1 of pet (Refer to ColorLabels dictionary)\n", "* **Color2** - Color 2 of pet (Refer to ColorLabels dictionary)\n", "* **Color3** - Color 3 of pet (Refer to ColorLabels dictionary)\n", "* **MaturitySize** - Size at maturity (1 = Small, 2 = Medium, 3 = Large, 4 = Extra Large, 0 = Not Specified)\n", "* **FurLength** - Fur length (1 = Short, 2 = Medium, 3 = Long, 0 = Not Specified)\n", "* **Vaccinated** - Pet has been vaccinated (1 = Yes, 2 = No, 3 = Not Sure)\n", "* **Dewormed** - Pet has been dewormed (1 = Yes, 2 = No, 3 = Not Sure)\n", "* **Sterilized** - Pet has been spayed / neutered (1 = Yes, 2 = No, 3 = Not Sure)\n", "* **Health** - Health Condition (1 = Healthy, 2 = Minor Injury, 3 = Serious Injury, 0 = Not Specified)\n", "* **Quantity** - Number of pets represented in profile\n", "* **Fee** - Adoption fee (0 = Free)\n", "* **State** - State location in Malaysia (Refer to StateLabels dictionary)\n", "* **RescuerID** - Unique hash ID of rescuer\n", "* **VideoAmt** - Total uploaded videos for this pet\n", "* **PhotoAmt** - Total uploaded photos for this pet\n", "* **Description** - Profile write-up for this pet. The primary language used is English, with some in Malay or Chinese.\n", "\n", "\n", "\n", "***AdoptionSpeed***\n", "* Contestants are required to predict this value. The value is determined by how quickly, if at all, a pet is adopted. The values are determined in the following way: \n", "* 0 - Pet was adopted on the same day as it was listed. \n", "* 1 - Pet was adopted between 1 and 7 days (1st week) after being listed. \n", "* 2 - Pet was adopted between 8 and 30 days (1st month) after being listed. \n", "* 3 - Pet was adopted between 31 and 90 days (2nd & 3rd month) after being listed. \n", "* 4 - No adoption after 100 days of being listed. (There are no pets in this dataset that waited between 90 and 100 days)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "9d073882a06a1e5a00e0786e3fa6285db2f24156" }, "source": [ "## Data Overview" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a", "collapsed": true }, "outputs": [], "source": [ "import os\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.ticker import PercentFormatter\n", "import seaborn as sns\n", "import missingno as msno\n", "import statsmodels.api as sm\n", "import scipy as sp \n", "\n", "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.metrics import cohen_kappa_score, make_scorer\n", "from sklearn.ensemble import GradientBoostingClassifier, AdaBoostClassifier, ExtraTreesClassifier, RandomForestClassifier\n", "from xgboost.sklearn import XGBClassifier\n", "from sklearn.model_selection import GridSearchCV\n", "\n", "%matplotlib inline\n", "sns.set_style(\"whitegrid\") \n", "plt.rcParams[\"figure.figsize\"] = [10,5]\n", "\n", "\n", "\n", "train = pd.read_csv('../input/train/train.csv')\n", "test = pd.read_csv('../input/test/test.csv')\n", "states = pd.read_csv('../input/state_labels.csv')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_uuid": "f040d9108ffa238656642686d50be0ed562783a3", "collapsed": true }, "outputs": [], "source": [ "def count_overview(variable, title):\n", " plt.figure(figsize=(10,4))\n", " ax = sns.barplot(x=variable, y = variable.index, palette=\"Spectral\")\n", " ax.set_title(title)\n", " plt.xlabel('Count')\n", " plt.tight_layout()\n", " plt.show()\n", " \n", "def stacked_barplot(tab, title):\n", " tab_percent = tab.apply(lambda r: r/r.sum(), axis=1)\n", " ax = tab_percent.plot.barh(stacked=True, cmap='Spectral', alpha=0.8)\n", " ax.set_title(title)\n", " vals = ax.get_yticks()\n", " ax.xaxis.set_major_formatter(PercentFormatter(xmax=1, decimals=0))\n", " ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1), shadow=True, ncol=5)\n", " plt.tight_layout()\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "9677d12182810a0132b32688e836324224eb07ad" }, "source": [ "### Training Dataset Overview" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_uuid": "8ef2a738dd2159b696f8cd194b266533a3ce6975" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training Dataset Shape: (14993, 24)\n" ] }, { "data": { "text/html": [ "
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TypeNameAgeBreed1Breed2GenderColor1Color2Color3MaturitySizeFurLengthVaccinatedDewormedSterilizedHealthQuantityFeeStateRescuerIDVideoAmtDescriptionPetIDPhotoAmtAdoptionSpeed
02Nibble3299011701122211100413268480853f516546f6cf33aa88cd76c3790Nibble is a 3+ month old ball of cuteness. He ...86e1089a31.02
12No Name Yet12650112022333110414013082c7125d8fb66f7dd4bff4192c8b140I just found it alone yesterday near my apartm...6296e909a2.00
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TypeAgeBreed1Breed2GenderColor1Color2Color3MaturitySizeFurLengthVaccinatedDewormedSterilizedHealthQuantityFeeStateVideoAmtPhotoAmtAdoptionSpeed
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mean1.45761410.452078265.27259474.0097381.7761622.2341763.2228371.8820121.8620021.4674851.7312081.5587271.9142271.0366171.57606921.25998841346.0283470.0567603.8892152.516441
std0.49821718.15579060.056818123.0115750.6815921.7452252.7425622.9840860.5479590.5990700.6676490.6958170.5661720.1995351.47247778.41454832.4441530.3461853.4878101.177265
min1.0000000.0000000.0000000.0000001.0000001.0000000.0000000.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000000.00000041324.0000000.0000000.0000000.000000
25%1.0000002.000000265.0000000.0000001.0000001.0000000.0000000.0000002.0000001.0000001.0000001.0000002.0000001.0000001.0000000.00000041326.0000000.0000002.0000002.000000
50%1.0000003.000000266.0000000.0000002.0000002.0000002.0000000.0000002.0000001.0000002.0000001.0000002.0000001.0000001.0000000.00000041326.0000000.0000003.0000002.000000
75%2.00000012.000000307.000000179.0000002.0000003.0000006.0000005.0000002.0000002.0000002.0000002.0000002.0000001.0000001.0000000.00000041401.0000000.0000005.0000004.000000
max2.000000255.000000307.000000307.0000003.0000007.0000007.0000007.0000004.0000003.0000003.0000003.0000003.0000003.00000020.0000003000.00000041415.0000008.00000030.0000004.000000
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" ], "text/plain": [ " Type Age ... PhotoAmt AdoptionSpeed\n", "count 14993.000000 14993.000000 ... 14993.000000 14993.000000\n", "mean 1.457614 10.452078 ... 3.889215 2.516441\n", "std 0.498217 18.155790 ... 3.487810 1.177265\n", "min 1.000000 0.000000 ... 0.000000 0.000000\n", "25% 1.000000 2.000000 ... 2.000000 2.000000\n", "50% 1.000000 3.000000 ... 3.000000 2.000000\n", "75% 2.000000 12.000000 ... 5.000000 4.000000\n", "max 2.000000 255.000000 ... 30.000000 4.000000\n", "\n", "[8 rows x 20 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"Training Dataset Shape: \", train.shape)\n", "display(train.head(2))\n", "display(train.info())\n", "display(train.describe())\n" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "9f16274a8e64eee108f89e1bb8228bc0759d6e65" }, "source": [ "### Test Dataset" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_uuid": "f0471b75c2ce17262b61ef47cbd3065c8cb7f154" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test Dataset Shape: (3948, 23)\n" ] }, { "data": { "text/html": [ "
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TypeNameAgeBreed1Breed2GenderColor1Color2Color3MaturitySizeFurLengthVaccinatedDewormedSterilizedHealthQuantityFeeStateRescuerIDVideoAmtDescriptionPetIDPhotoAmt
01Puppy2307011002222211150413264475f31553f0170229455e3c5645644f0Puppy is calm for a young dog, but he becomes ...378fcc4fc3.0
12London242660127021111110413264475f31553f0170229455e3c5645644f0Urgently seeking adoption. Please contact for ...73c10e1361.0
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TypeAgeBreed1Breed2GenderColor1Color2Color3MaturitySizeFurLengthVaccinatedDewormedSterilizedHealthQuantityFeeStateVideoAmtPhotoAmt
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mean1.52608911.564590263.03343557.3594221.7826752.2320163.3556232.0612971.8244681.4668191.7036471.5060791.8893111.0435661.62639327.34625141351.0192500.0628173.809524
std0.49938218.56842959.178121112.0868100.6926331.7366142.7001443.0413570.5697720.6133080.6642000.6829300.5879950.2185391.60991488.41604534.7086480.3913243.627959
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75%2.00000012.000000307.0000000.0000002.0000003.0000006.0000006.0000002.0000002.0000002.0000002.0000002.0000001.0000001.0000000.00000041401.0000000.0000005.000000
max2.000000180.000000307.000000307.0000003.0000007.0000007.0000007.0000004.0000003.0000003.0000003.0000003.0000003.00000020.0000002400.00000041401.0000009.00000030.000000
\n", "
" ], "text/plain": [ " Type Age ... VideoAmt PhotoAmt\n", "count 3948.000000 3948.000000 ... 3948.000000 3948.000000\n", "mean 1.526089 11.564590 ... 0.062817 3.809524\n", "std 0.499382 18.568429 ... 0.391324 3.627959\n", "min 1.000000 0.000000 ... 0.000000 0.000000\n", "25% 1.000000 2.000000 ... 0.000000 2.000000\n", "50% 2.000000 4.000000 ... 0.000000 3.000000\n", "75% 2.000000 12.000000 ... 0.000000 5.000000\n", "max 2.000000 180.000000 ... 9.000000 30.000000\n", "\n", "[8 rows x 19 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"Test Dataset Shape: \", test.shape)\n", "display(test.head(2))\n", "display(test.info())\n", "display(test.describe())" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "0a72fdcfb5a948d38f1bd252430f975a618597ad" }, "source": [ "Let's concatenate the datasets to make the EDA easier\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_uuid": "a3c648ef9feeb0217ca7af1489de8234df182f17", "collapsed": true }, "outputs": [], "source": [ "train_target = train['AdoptionSpeed']\n", "test_id = test['PetID']\n", "all_data = pd.concat((train, test), sort=True).reset_index(drop=True)" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2019-04-18T15:56:09.260639Z", "start_time": "2019-04-18T15:56:09.252643Z" }, "_uuid": "cb6f6df072cd67c546cad0957cc2fd8a13eb6e71" }, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "beaae8ff3357107aa1a995cf58944e5a0529bf05" }, "source": [ "## Missing Data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_uuid": "a9a6fc5b73001d8bc8d81ebcd45d72e9e33f4994" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "msno.bar(all_data.drop('AdoptionSpeed', axis=1), color=sns.color_palette(\"Spectral\", 5)[4])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "3cef60a09ebc6f300885435d015c0d128950ad52" }, "source": [ "* The only variable with missing values is the \"Name\". \n", "* My intuition is that people are more likely to respond positively to listed pets with existing names. Imagine a little girl saying:\n", " * \"Aww, look how cute '*Peanut*' is!\"\n", "\n", "Let's test the hypothesis that the name would play a role in the Adoption Speed. First, we define a new variable (\"NameorNot\"), then conduct a chi-squared enrichment analysis. Lastly, we examine the standardized residuals to see any effects." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_uuid": "4d7b691c4a2a90bf6d4a82fd8774a7802e6f21a0" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of Animals by Type\n" ] }, { "data": { "text/html": [ "
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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train['NameorNot'] = np.where(train['Name'].isnull(), 'No Name', 'Has a Name')\n", "test['NameorNot'] = np.where(test['Name'].isnull(), 'No Name', 'Has a Name')\n", "\n", "\n", "df_plot = train.groupby(['NameorNot', 'AdoptionSpeed']).size().reset_index().pivot(\n", " columns='AdoptionSpeed', index='NameorNot', values=0)\n", "print(\"Number of Animals by Type\")\n", "display(df_plot)\n", "\n", "ggDF = train['NameorNot'].value_counts()\n", "count_overview(ggDF, 'NameorNot Count')\n", "\n", "tab = pd.crosstab(train['NameorNot'], train['AdoptionSpeed'])\n", "table = sm.stats.Table(tab)\n", "\n", "print(\"Chi-Square Test of Independence: p-value = {:.3E}\".format(sp.stats.chi2_contingency(tab)[1]))\n", "display(table.standardized_resids)\n", "\n", "stacked_barplot(tab, \"Effects of a Name on Adoption Speed\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "0e28f5879a07ec4de5af42a55f496f5c5bb416e4" }, "source": [ "Looking at the standardized residuals table and the bottom plot, we can see that pets that are listed without a name are more likely to NOT be adopted.\n", "\n", "* As a rule of thumb, values outside the range of [-2, 2] exhibit a lower or higher than expected frequency, respectively." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "6f7a74a35d0e6538ebcf8c0a85bf9278199a19ed" }, "source": [ "## Variable Visualizations\n", "\n", "Let's split the training dataset into categorical and numeric subsets to make it easier to visualize" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "2b12ea8e730ed5bcf3b390cba0aa06015674db7d" }, "source": [ "### Target Variable (Adoption Speed) " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_uuid": "5bbb2e98f1280fe07e935bc5c1d23b132e8d07a5" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ggDF = pd.DataFrame(train['AdoptionSpeed'].value_counts().rename({0: '0 (Same Day)', 1: '1 (1-7 Days)', 2: '2 (8-30 Days)', 3: '3 (31-90 Days)', 4: '4 (No Adoption After 100 Days)'}))\n", "ggDF['Order'] = [4, 2, 3, 1, 0]\n", "ggDF.sort_values('Order', inplace=True, ascending=True)\n", "count_overview(ggDF['AdoptionSpeed'], 'AdoptionSpeed (Target Variable)')" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "50b56b1bc3089944c033f08a38dc24e63b7361e0" }, "source": [ "### Cats vs Dogs" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_uuid": "a1dfcf800f025a7107bfed2f2e2895dcad974dc9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of Animals by Type\n" ] }, { "data": { "text/html": [ "
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AdoptionSpeed01234
Type
Dog1701435216419492414
Cat2401655187313101783
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" ], "text/plain": [ "AdoptionSpeed 0 1 2 3 4\n", "Type \n", "Dog 170 1435 2164 1949 2414\n", "Cat 240 1655 1873 1310 1783" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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AdoptionSpeed01234
Type
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" ], "text/plain": [ "AdoptionSpeed 0 1 2 3 4\n", "Type \n", "Dog -5.264749 -9.7657 -0.946594 7.208134 5.024245\n", "Cat 5.264749 9.7657 0.946594 -7.208134 -5.024245" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_plot = train.groupby(['Type', 'AdoptionSpeed']).size().reset_index().pivot(columns='AdoptionSpeed', index='Type', values=0).rename({1: 'Dog', 2: 'Cat'})\n", "print(\"Number of Animals by Type\")\n", "display(df_plot)\n", "\n", "ggDF = train['Type'].value_counts().rename({1: 'Dog', 2: 'Cat'})\n", "count_overview(ggDF, 'Type Count')\n", "\n", "tab = pd.crosstab(train['Type'], train['AdoptionSpeed']).rename({1: 'Dog', 2: 'Cat'})\n", "table = sm.stats.Table(tab)\n", "\n", "print(\"Chi-Square Test of Independence: p-value = {:.3E}\".format(sp.stats.chi2_contingency(tab)[1]))\n", "display(table.standardized_resids)\n", "\n", "stacked_barplot(tab, \"Adoption Speed by Type\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "74ace1e07a8218f1a786a2ea2de63f8ec14e20d0" }, "source": [ "It seems cats are more likely to be adopted sooner, compared to dogs. This may be due to the larger responsibility & commitment associated with owning a dog." ] }, { "cell_type": "markdown", "metadata": { "_uuid": "76a65bf32469aa7593044307bc31c1e635479786" }, "source": [ "### Gender" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_uuid": "b3eb86a86bcdd6e123ba6ee0a2a11d8ad9563866" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of Animals by Gender\n" ] }, { "data": { "text/html": [ "
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AdoptionSpeed01234
Gender
Male1601283157811091406
Female2041366191116712125
Mixed (Groups of Pets)46441548479666
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" ], "text/plain": [ "AdoptionSpeed 0 1 2 3 4\n", "Gender \n", "Male 160 1283 1578 1109 1406\n", "Female 204 1366 1911 1671 2125\n", "Mixed (Groups of Pets) 46 441 548 479 666" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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AdoptionSpeed01234
Gender
Male0.8936095.9428753.333695-3.871096-5.416289
Female0.501221-5.403628-1.7829103.5344353.200882
Mixed (Groups of Pets)-1.934038-0.474798-2.0361830.2885812.876952
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" ], "text/plain": [ "AdoptionSpeed 0 1 ... 3 4\n", "Gender ... \n", "Male 0.893609 5.942875 ... -3.871096 -5.416289\n", "Female 0.501221 -5.403628 ... 3.534435 3.200882\n", "Mixed (Groups of Pets) -1.934038 -0.474798 ... 0.288581 2.876952\n", "\n", "[3 rows x 5 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_plot = train.groupby(['Gender', 'AdoptionSpeed']).size().reset_index().pivot(\n", " columns='AdoptionSpeed', index='Gender', values=0).rename({1: 'Male', 2: 'Female', 3: 'Mixed (Groups of Pets)'})\n", "print(\"Number of Animals by Gender\")\n", "display(df_plot)\n", "\n", "ggDF = train['Gender'].value_counts().rename({1: 'Male', 2: 'Female', 3: 'Mixed (Groups of Pets)'})\n", "count_overview(ggDF, 'Gender Count')\n", "\n", "tab = pd.crosstab(train['Gender'], train['AdoptionSpeed']).rename({1: 'Male', 2: 'Female', 3: 'Mixed (Groups of Pets)'})\n", "table = sm.stats.Table(tab)\n", "print(\"Chi-Square Test of Independence: p-value = {:.3E}\".format(sp.stats.chi2_contingency(tab)[1]))\n", "display(table.standardized_resids)\n", "\n", "stacked_barplot(tab, \"Adoption Speed by Type\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_uuid": "fb890c4fedda649e40b02d41824172b3499f9877" }, "outputs": [ { "data": { "image/png": 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Jyck3Oz8AAACg33V757mhoUHLli2TdG3Jwpw5c/T1r39dDz30kHJyclRQUKCRI0dqy5YtkqQZM2aotLRULpdLgwcPVm5uriQpOjpaS5cuVWZmpiRp2bJlio6O7qt5AQAAAL2u2/AcHx+vt956q8P48OHDtWPHjg7jNptNa9as6fRYmZmZgfAMAAAADDS8YRAAAAAwRHgGAAAADBGeAQAAAEOEZwAAAMAQ4RkAAAAwRHgGAAAADBGeAQAAAEOEZwAAAMAQ4RkAAAAwRHgGAAAADBGeAQAAAEOEZwAAAMAQ4RkAAAAwRHgGAAAADBGeAQAAAEOEZwAAAMAQ4RkAAAAwRHgGAAAADBGeAQAAAEOEZwAAAMAQ4RkAAAAwRHgGAAAADBGeAQAAAEOEZwAAAMAQ4RkAAAAwRHgGAAAADBGeAQAAAEOEZwAAAMAQ4RkAAAAwRHgGAAAADBGeAQAAAEOEZwAAAMAQ4RkAAAAwRHgGAAAADBmHZ7/fr4yMDP3jP/6jJKm2tlZZWVlyuVzKyclRa2urJKm1tVU5OTlyuVzKyspSXV1d4Bjbtm2Ty+VSamqqysrKenkqAAAAQN8yDs+/+tWvNHbs2MDvN23apEWLFunAgQOKiopSQUGBJGn37t2KiorSgQMHtGjRIm3atEmSdOrUKRUVFamoqEjbt2/X2rVr5ff7e3k6AAAAQN8xCs/19fU6fPiwMjMzJUmWZeno0aNKTU2VJM2bN08lJSWSpEOHDmnevHmSpNTUVL3//vuyLEslJSVKS0tTRESE4uPjNXr0aFVVVfXFnAAAAIA+YRSec3Nz9eyzz2rQoGu7nzt3TlFRUQoPD5ckOZ1Oeb1eSZLX69WIESMkSeHh4Ro6dKjOnTsnr9crp9MZOKbD4Qh8BgAAABgIwrvb4Xe/+51iYmL04IMP6oMPPuiPmgJaWlpUXV3dr+f81IjRowPruK/n9/uDVhN6x5UrV+hhiKK3oY3+hi56G9pCrb/dhuf//M//1KFDh3TkyBG1tLTowoUL2rhxo5qbm9Xe3q7w8HDV19fL4XBIunZH+cyZM3I6nWpvb9f58+c1fPhwORwO1dfXB47r9XoDn+lKZGSkxo8ff4tTvDlNly8rIiKiw3hYWFjQakLvqK6upochit6GNvobuuhtaBso/TUN+N0u2/jhD3+oI0eO6NChQ3r11Vf18MMP66c//akSEhK0f/9+SVJhYaFSUlIkSSkpKSosLJQk7d+/Xw8//LBsNptSUlJUVFSk1tZW1dbWqqamRhMnTrzZ+QEAAAD97qaf8/zss8/q9ddfl8vlUmNjo7KysiRJmZmZamxslMvl0uuvv65nnnlGkjRu3Dg9/vjjmj17tr7//e9r9erVCgsL651ZAAAAAP2g22Ub10tISFBCQoIkKT4+PvB4uutFRkZq69atnX5+yZIlWrJkyU2UCQAAAAQfbxgEAAAADBGeAQAAAEOEZwAAAMAQ4RkAAAAwRHgGAAAADBGeAQAAAEOEZwAAAMAQ4RkAAAAwRHgGAAAADPXoDYO45kJra4exQTabvnDXXUGoBgAAAP2F8NxDlqRVpYc7jOfOeKy/SwEAAEA/Y9kGAAAAYIjwDAAAABgiPAMAAACGCM8AAACAIcIzAAAAYIjwDAAAABgiPAMAAACGCM8AAACAIcIzAAAAYIjwDAAAABgiPAMAAACGCM8AAACAIcIzAAAAYIjwDAAAABgiPAMAAACGCM8AAACAIcIzAAAAYIjwDAAAABgiPAMAAACGCM8AAACAIcIzAAAAYIjwDAAAABgiPAMAAACGug3PLS0tyszM1Le+9S2lpaVp69atkqTa2lplZWXJ5XIpJydHra2tkqTW1lbl5OTI5XIpKytLdXV1gWNt27ZNLpdLqampKisr66MpAQAAAH2j2/AcERGhHTt26K233pLH41FZWZkqKyu1adMmLVq0SAcOHFBUVJQKCgokSbt371ZUVJQOHDigRYsWadOmTZKkU6dOqaioSEVFRdq+fbvWrl0rv9/ft7MDAAAAelG34dlms+mee+6RJLW3t6u9vV02m01Hjx5VamqqJGnevHkqKSmRJB06dEjz5s2TJKWmpur999+XZVkqKSlRWlqaIiIiFB8fr9GjR6uqqqqv5gUAAAD0OqM1z36/X3PnztUjjzyiRx55RPHx8YqKilJ4eLgkyel0yuv1SpK8Xq9GjBghSQoPD9fQoUN17tw5eb1eOZ3OwDEdDkfgMwAAAMBAEG6yU1hYmPbu3avm5mYtW7ZMf/7zn/u6LknX1ltXV1f3y7k+a8To0YF13DewrE7H/X5/0GpFz1y5coVehSh6G9rob+iit6Et1PprFJ4/FRUVpYSEBFVWVqq5uVnt7e0KDw9XfX29HA6HpGt3lM+cOSOn06n29nadP39ew4cPl8PhUH19feBYXq838JmuREZGavz48TcxrVvXdPmyIiIiOm6w2TodDwsLC1qt6Jnq6mp6FaLobWijv6GL3oa2gdJf04Df7bKNTz75RM3NzZKufefw+9//XmPHjlVCQoL2798vSSosLFRKSookKSUlRYWFhZKk/fv36+GHH5bNZlNKSoqKiorU2tqq2tpa1dTUaOLEiTc1OQAAACAYur3z7PP59Nxzz8nv98uyLM2aNUvf+MY3dP/992vFihXasmWLxo8fr6ysLElSZmamnn32WblcLg0bNkybN2+WJI0bN06PP/64Zs+erbCwMK1evVphYWF9OzsAAACgF3Ubnr/yla/I4/F0GI+Pjw88nu56kZGRgWdBf9aSJUu0ZMmSmygTAAAACD7eMAgAAAAYIjwDAAAAhgjPAAAAgCHCMwAAAGCI8AwAAAAYIjwDAAAAhgjPAAAAgCHCMwAAAGCI8AwAAAAYIjwDAAAAhgjPAAAAgCHCMwAAAGCI8AwAAAAYIjwDAAAAhgjPAAAAgCHCMwAAAGCI8AwAAAAYIjwDAAAAhgjPAAAAgCHCMwAAAGCI8AwAAAAYIjwDAAAAhgjPAAAAgCHCMwAAAGCI8AwAAAAYIjwDAAAAhgjPAAAAgCHCMwAAAGCI8AwAAAAYIjwDAAAAhgjPAAAAgCHCMwAAAGCI8AwAAAAYIjwDAAAAhgjPAAAAgKFuw/OZM2e0cOFCzZ49W2lpadqxY4ckqbGxUdnZ2XK73crOzlZTU5MkybIsbdiwQS6XS+np6Tpx4kTgWIWFhXK73XK73SosLOyjKQEAAAB9o9vwHBYWpueee0779u3Tm2++qd/85jc6deqU8vPzlZiYqOLiYiUmJio/P1+SdOTIEdXU1Ki4uFjr16/Xiy++KOla2M7Ly9OuXbu0e/du5eXlBQI3AAAAMBB0G57tdrsmTJggSRoyZIjGjBkjr9erkpISZWRkSJIyMjJ08OBBSQqM22w2TZ48Wc3NzfL5fCovL1dSUpKio6M1bNgwJSUlqaysrA+nBgAAAPSu8J7sXFdXp+rqak2aNEkNDQ2y2+2SpLi4ODU0NEiSvF6vnE5n4DNOp1Ner7fDuMPhkNfr/dzztbS0qLq6uicl9poRo0ertbW14wbL6nTc7/cHrVb0zJUrV+hViKK3oY3+hi56G9pCrb/G4fnixYtavny5XnjhBQ0ZMuSGbTabTTabrdeLi4yM1Pjx43v9uCaaLl9WRERExw02W6fjYWFhQasVPVNdXU2vQhS9DW30N3TR29A2UPprGvCNnrbR1tam5cuXKz09XW63W5IUGxsrn88nSfL5fIqJiZF07Y5yfX194LP19fVyOBwdxr1erxwOh9lsAAAAgNtAt+HZsiytWrVKY8aMUXZ2dmA8JSVFHo9HkuTxeDRz5swbxi3LUmVlpYYOHSq73a7k5GSVl5erqalJTU1NKi8vV3Jych9NCwAAAOh93S7b+I//+A/t3btXX/7ylzV37lxJ0sqVK7V48WLl5OSooKBAI0eO1JYtWyRJM2bMUGlpqVwulwYPHqzc3FxJUnR0tJYuXarMzExJ0rJlyxQdHd1X8wIAAAB6XbfheerUqfrjH//Y6bZPn/l8PZvNpjVr1nS6f2ZmZiA8AwAAAAMNbxgEAAAADBGeAQAAAEOEZwAAAMAQ4RkAAAAwRHgGAAAADBGeAQAAAEOEZwAAAMAQ4RkAAAAwRHgGAAAADBGeAQAAAEOEZwAAAMAQ4RkAAAAwRHgGAAAADBGeAQAAAEOEZwAAAMAQ4RkAAAAwRHgGAAAADBGeAQAAAEOEZwAAAMAQ4RkAAAAwRHgGAAAADBGeAQAAAEOEZwAAAMAQ4RkAAAAwRHgGAAAADBGeAQAAAEOEZwAAAMAQ4RkAAAAwRHgGAAAADBGeAQAAAEOEZwAAAMAQ4RkAAAAwRHgGAAAADBGeAQAAAEPdhufnn39eiYmJmjNnTmCssbFR2dnZcrvdys7OVlNTkyTJsixt2LBBLpdL6enpOnHiROAzhYWFcrvdcrvdKiws7IOpAAAAAH2r2/A8f/58bd++/Yax/Px8JSYmqri4WImJicrPz5ckHTlyRDU1NSouLtb69ev14osvSroWtvPy8rRr1y7t3r1beXl5gcB9p7rU1qYLra0dfl1qawt2aQAAAOhCeHc7TJs2TXV1dTeMlZSU6Ne//rUkKSMjQwsXLtSzzz6rkpISZWRkyGazafLkyWpubpbP51NFRYWSkpIUHR0tSUpKSlJZWdkNd7PvNFctSy+UHu4wnjvjsX6vBQAAAGZuas1zQ0OD7Ha7JCkuLk4NDQ2SJK/XK6fTGdjP6XTK6/V2GHc4HPJ6vbdSNwAAANDvur3z3B2bzSabzdYbtXTQ0tKi6urqPjl2d0aMHq3W1taOGyyr03G/39+jWkfee2+vHAc9d+XKFf6MQxS9DW30N3TR29AWav29qfAcGxsrn88nu90un8+nmJgYSdfuKNfX1wf2q6+vl8PhkMPhUEVFRWDc6/Vq+vTp3Z4nMjJS48ePv5kSb1nT5cuKiIjouMFm63Q8LCysR7VeaG3tleOg56qrq/kzDlH0NrT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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,5))\n", "sns.distplot(train['Age'], kde=False, bins=100, hist_kws=dict(alpha=0.85), color=sns.color_palette(\"Spectral\", 5)[4]).set_title('Age Distribution')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "a277a29156e974d9c586b5c3ea69a5ac82a3942c" }, "source": [ "There seems to be a large number of outliers (very old pets), but the median value (50th percentile) is the same for both cats and dogs. The average age of pets up for adoption is 3 months. \n", "\n", "* *Fun fact*: This coincidences with the end of socialization period and the start of the juvenile period in dogs. Cats on the other hand are still considered kittens for another 3 months." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_uuid": "a4f52ebb249efdc2ed456696be3de58c3ed55098" }, "outputs": [ { "data": { "text/html": [ "
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Age
countmeanstdmin25%50%75%max
AdoptionSpeed
0410.010.45122017.7751180.02.03.012.0120.0
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" ], "text/plain": [ " Age \n", " count mean std min 25% 50% 75% max\n", "AdoptionSpeed \n", "0 410.0 10.451220 17.775118 0.0 2.0 3.0 12.0 120.0\n", "1 3090.0 8.488350 15.746187 0.0 2.0 2.0 6.0 147.0\n", "2 4037.0 8.823631 16.779013 0.0 2.0 3.0 6.0 156.0\n", "3 3259.0 10.189936 18.672104 0.0 2.0 3.0 9.0 212.0\n", "4 4197.0 13.667858 20.177460 0.0 3.0 6.0 15.0 255.0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(train[['AdoptionSpeed', 'Age']].groupby('AdoptionSpeed').describe())\n", "sns.barplot(x=train.AdoptionSpeed, y=train.Age, palette = 'Spectral')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "b44f279179824fbdc4bc1d04c6831bf8241ae4b7" }, "source": [ "As expected, pets that are not adopted after 100 days are on average much older pets. Interestingly, the pets that were adopted on the same day ranged in all ages. There must be another factor in play that makes people adopt immediately within one day.\n" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "c6565c5448f3a849a1bc05457017c0794ff1292c" }, "source": [ "### PhotoAmt - Can you have too many cute pictures? \n", "\n", " Spoiler Alert: You can't!" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_uuid": "0f41770aed6790341863de7d93dd8071de5a3e4a" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,5))\n", "sns.distplot(train['PhotoAmt'], kde=False, bins=30, hist_kws=dict(alpha=0.85), color=sns.color_palette(\"Spectral\", 5)[4]).set_title('PhotoAmt Distribution')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "2c3b189d3dbc576348f34e80a15cb42e3188b265" }, "source": [ "It's highly unlikely that the difference between 7 and 8 pictures will be the deciding factor for adoption, but we can group the number of photos into a new variable (***PhotosType***).\n", "\n", "The threshold for the number of photos for each class were somewhat arbitrarily, but the histogram above shows a steep drop in PhotoAmt after 5. The ***PhotoType*** classes correspond to the following number of pictures:\n", "> * *No Photos*: 0 photos available\n", "> * *Few Photos*: Between 1-5 photos\n", "> * *Many Photos*: More than 5 photos" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_uuid": "6342889727da8a0a6b4441b6bae09ce5d142ad4c", "collapsed": true }, "outputs": [], "source": [ "train['PhotosType'] = \"No Photos\"\n", "train['PhotosType'] = np.where(train['PhotoAmt']>1, 'Few Photos', train['PhotosType'])\n", "train['PhotosType'] = np.where(train['PhotoAmt']>5, 'Many Photos', train['PhotosType'])\n", "\n", "test['PhotosType'] = \"No Photos\"\n", "test['PhotosType'] = np.where(test['PhotoAmt']>1, 'Few Photos', test['PhotosType'])\n", "test['PhotosType'] = np.where(test['PhotoAmt']>5, 'Many Photos', test['PhotosType'])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_uuid": "70ba92a4848410e0de9bfeb64a29785fe704b70c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of Pets Adopted by Number of Photos Available\n" ] }, { "data": { "text/html": [ "
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AdoptionSpeed01234
PhotosType
Few Photos2682003246018182508
Many Photos43438758815466
No Photos996498196261223
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" ], "text/plain": [ "AdoptionSpeed 0 1 2 3 4\n", "PhotosType \n", "Few Photos 268 2003 2460 1818 2508\n", "Many Photos 43 438 758 815 466\n", "No Photos 99 649 819 626 1223" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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AdoptionSpeed01234
PhotosType
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_plot = train.groupby(['PhotosType', 'AdoptionSpeed']).size().reset_index().pivot(\n", " columns='AdoptionSpeed', index='PhotosType', values=0)\n", "print(\"Number of Pets Adopted by Number of Photos Available\")\n", "display(df_plot)\n", "\n", "ggDF = train['PhotosType'].value_counts()\n", "count_overview(ggDF, 'Photos Type Count')\n", "\n", "tab = pd.crosstab(train['PhotosType'], train['AdoptionSpeed'])\n", "table = sm.stats.Table(tab)\n", "print(\"Chi-Square Test of Independence: p-value = {:.3E}\".format(sp.stats.chi2_contingency(tab)[1]))\n", "display(table.standardized_resids)\n", "\n", "stacked_barplot(tab, \"Adoption Speed by Number of Photos\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "0365defa1b7cfe3cf6fa63bc37d0b42adb857202" }, "source": [ "Having no photos seems to be a big factor associated with pets not being adopted after 100 days." ] }, { "cell_type": "markdown", "metadata": { "_uuid": "8cdefc1b09c924c9f37045fe5c6f15e3a69d68f5" }, "source": [ "### Location (Location, Location)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "_uuid": "7d575ca9a69f4b909790e4c771a88fd61b101685" }, "outputs": [ { "data": { "text/html": [ "
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AdoptionSpeed01234Total
State
Melaka418231280137
Kedah314342336110
Selangor24618772435200421528714
Pulau Pinang8122216197300843
Perak348111117141420
Negeri Sembilan4366342108253
Pahang32914162385
Johor23113136103132507
Sarawak1102913
Sabah1634822
Terengganu0926926
Kelantan2331615
Kuala Lumpur11281499673111923845
Labuan001113
\n", "
" ], "text/plain": [ "AdoptionSpeed 0 1 2 3 4 Total\n", "State \n", "Melaka 4 18 23 12 80 137\n", "Kedah 3 14 34 23 36 110\n", "Selangor 246 1877 2435 2004 2152 8714\n", "Pulau Pinang 8 122 216 197 300 843\n", "Perak 3 48 111 117 141 420\n", "Negeri Sembilan 4 36 63 42 108 253\n", "Pahang 3 29 14 16 23 85\n", "Johor 23 113 136 103 132 507\n", "Sarawak 1 1 0 2 9 13\n", "Sabah 1 6 3 4 8 22\n", "Terengganu 0 9 2 6 9 26\n", "Kelantan 2 3 3 1 6 15\n", "Kuala Lumpur 112 814 996 731 1192 3845\n", "Labuan 0 0 1 1 1 3" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "states = pd.read_csv('../input/state_labels.csv')\n", "states.index = states['StateID']\n", "states_dict = states.to_dict()\n", "\n", "state_counts = pd.crosstab(train['State'], train['AdoptionSpeed'])\n", "state_counts.index = state_counts.index.map(states_dict['StateName'])\n", "\n", "state_totals = state_counts.copy(deep=True)\n", "state_totals['Total'] = state_counts.sum(axis=1)\n", "display(state_totals)\n", "\n", "\n", "plt.figure(figsize=(10,6))\n", "ax = sns.barplot(x=state_totals.Total, y = state_totals.index, palette=\"Spectral\")\n", "ax.set_title(\"Adoptions by State\")\n", "plt.xlabel('Count')\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "\n", "stacked_barplot(state_counts, 'Adoption Speed by State')" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "dd7d7bfa24936f45a5ac0ac1fc3f5c657de294ba" }, "source": [ "There is a clear difference in adoption speeds between the different states. Sarawak and Melaka have the highest proportion of pets left unadopted after 100 days compared to Kelatan and Sarawak, which have the lowest proportions." ] }, { "cell_type": "markdown", "metadata": { "_uuid": "408848a82821c40509caa6b7c9c8e207978f28f3" }, "source": [ "### Purebreed vs Mudblood Mutts?" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "_uuid": "bb871f981d8e1df59b43313ac63b2d23a04fe0fa" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of Pets Adopted by Number of Photos Available\n" ] }, { "data": { "text/html": [ "
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Mixed Breed15787611339921073
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" ], "text/plain": [ "AdoptionSpeed 0 1 2 3 4\n", "Purebreed \n", "Mixed Breed 157 876 1133 992 1073\n", "Pure Breed 253 2214 2904 2267 3124" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Purebreed
Mixed Breed4.5950.179756-0.2550533.181495-4.501907
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" ], "text/plain": [ "AdoptionSpeed 0 1 2 3 4\n", "Purebreed \n", "Mixed Breed 4.595 0.179756 -0.255053 3.181495 -4.501907\n", "Pure Breed -4.595 -0.179756 0.255053 -3.181495 4.501907" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train['Purebreed'] = np.where(train['Breed2'] == 0, 'Pure Breed', 'Mixed Breed')\n", "test['Purebreed'] = np.where(test['Breed2'] == 0, 'Pure Breed', 'Mixed Breed')\n", "\n", "df_plot = train.groupby(['Purebreed', 'AdoptionSpeed']).size().reset_index().pivot(\n", " columns='AdoptionSpeed', index='Purebreed', values=0)\n", "print(\"Number of Pets Adopted by Number of Photos Available\")\n", "display(df_plot)\n", "\n", "ggDF = train['Purebreed'].value_counts()\n", "count_overview(ggDF, 'Photos Type Count')\n", "\n", "tab = pd.crosstab(train['Purebreed'], train['AdoptionSpeed'])\n", "table = sm.stats.Table(tab)\n", "print(\"Chi-Square Test of Independence: p-value = {:.3E}\".format(sp.stats.chi2_contingency(tab)[1]))\n", "display(table.standardized_resids)\n", "\n", "stacked_barplot(tab, \"Adoption Speed by Number of Photos\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "71804e5c37c20b21345f5b3ac2e2ed627b587c1a" }, "source": [ "Mixed breeds seem to be adopted faster than pure breeds" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "6d0fb256303a6bc115691922937536f91e67350f" }, "source": [ "### What's better than free?\n", "\n", "First, let's see the distribution of fees" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "_uuid": "272fde3ebae03fcc3dda2352faec16d95d4c7e3f" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.distplot(train['Fee'], kde=False, bins=50, hist_kws=dict(alpha=0.85), color=sns.color_palette(\"Spectral\", 5)[4])\n", "ax.set_title('Fee Distribution (Log Scale)')\n", "ax.set_yscale('log')" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "1ddd2057a743ed73f6cebe2b3037ec851d33dcee" }, "source": [ "* *Note*: The fee cost on the y-axis is in log scale. So there is several thousand more pets up for adoption with no cost than at any price.\n", "\n", "Let's convert the fee variable into a new categorical variable (***PayorNot***). This group is composed of:\n", "> * Free - No fee associated with adoption\n", "> * Paid Adoption - Fee associated with adoption " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "_uuid": "eaada0dac1fcb655550fcf386999e98b6e601e8a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of Pets Adopted by Number of Photos Available\n" ] }, { "data": { "text/html": [ "
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PayorNot
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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train['PayorNot'] = np.where(train['Fee'] == 0, 'Free', 'Paid Adoption')\n", "test['PayorNot'] = np.where(test['Fee'] == 0, 'Free', 'Paid Adoption')\n", "\n", "\n", "df_plot = train.groupby(['PayorNot', 'AdoptionSpeed']).size().reset_index().pivot(\n", " columns='AdoptionSpeed', index='PayorNot', values=0).rename({0: 'Free', 1: 'Paid Adoption'})\n", "print(\"Number of Pets Adopted by Number of Photos Available\")\n", "display(df_plot)\n", "\n", "ggDF = train['PayorNot'].value_counts()\n", "count_overview(ggDF, 'Photos Type Count')\n", "\n", "tab = pd.crosstab(train['PayorNot'], train['AdoptionSpeed'])\n", "table = sm.stats.Table(tab)\n", "print(\"Chi-Square Test of Independence: p-value = {:.3E}\".format(sp.stats.chi2_contingency(tab)[1]))\n", "display(table.standardized_resids)\n", "\n", "stacked_barplot(tab, \"Adoption Speed by Number of Photos\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "e9c166cc4016160b6dfe4221524baeb22a50519f" }, "source": [ "* The effects of a fee seems negligible, but there is some evidence that pets that are not adopted after the 100 days are likely to have a fee associated with them. This might be due to the adoption shelters attempting to recoup their costs." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "_uuid": "d267a6e0fd7c504e52cee239c94148b9b6be2cb3" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,5))\n", "sns.distplot(train[train['PayorNot'] != 'Free']['Fee'], kde=False, bins=30, hist_kws=dict(alpha=0.85), color=sns.color_palette(\"Spectral\", 5)[4]).set_title('Age Distribution')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "5dcc9316260dcd5952a51d047f52574460407425" }, "source": [ "### Length of Description" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "_uuid": "0e4c4eb7643c2c31967694ab0f4da86d7f9bbd1f" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train['DescriptionLength'] = train['Description'].apply(lambda x: len(str(x)))\n", "sns.distplot(train['DescriptionLength'], kde=False, bins=50, hist_kws=dict(alpha=0.85), color=sns.color_palette(\"Spectral\", 5)[4])\n", "plt.show()\n", "\n", "test['DescriptionLength'] = test['Description'].apply(lambda x: len(str(x)))\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "_uuid": "a27e77603d626214e6052f16cf0b52e8020df263" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(x = train['AdoptionSpeed'], y = train['DescriptionLength'])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "_uuid": "eafdd311ad642377b67979a35606b864015424cb" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gr = train.groupby('AdoptionSpeed').DescriptionLength\n", "for label, arr in gr:\n", " sns.kdeplot(arr, label=label, shade=True)" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2019-04-18T15:57:36.556305Z", "start_time": "2019-04-18T15:57:36.548290Z" } }, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "5727b715b900a27371949feee52bee75e2b0872a" }, "source": [ "## Caution Variables\n", "\n", "Some of the variables, such as *MaturitySize* or *FurLength*, include labels that are either \"Not Specified\" or \"Not Sure\". We will impute these unknown values are the mode (or most common variable)." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "_uuid": "8b523ea8df514dac48a0509ebc02e7b38c154b1f", "collapsed": true }, "outputs": [], "source": [ "caution_variables = ['MaturitySize', 'FurLength', 'Health', 'Vaccinated', 'Dewormed', 'Sterilized', 'Health']\n", "\n", "for variable in caution_variables:\n", " train[variable].replace(0, train[variable].mode()[0], regex=True, inplace=True)\n", " test[variable].replace(0, test[variable].mode()[0], regex=True, inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "b5fa70f5b2c531531572162cac9c7df1734718e0" }, "source": [ "## Data Cleaning" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "_uuid": "6daaf56bce4e89ccf4958a5f0010ae650e0b6932", "collapsed": true }, "outputs": [], "source": [ "clean_train = train.drop(columns=['Name', 'RescuerID', 'Description', 'PetID', 'AdoptionSpeed'])\n", "clean_test = test.drop(columns=['Name', 'RescuerID', 'Description', 'PetID'])" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "9a42c6e311c117a35ed031d08a506cffe4d1a120" }, "source": [ "### Label Encoding" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "_uuid": "599e439e15581e9109ad1302946aa0b42a73c356", "collapsed": true }, "outputs": [], "source": [ "cat_cols = ['PayorNot', 'NameorNot','PhotosType','Purebreed']\n", "\n", "for col in cat_cols:\n", " label = LabelEncoder()\n", " label.fit(list(clean_train[col].values))\n", " clean_train[col] = label.transform(list(clean_train[col].values))\n", " clean_test[col] = label.transform(list(clean_test[col].values))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "8c304d59e51427f6547adbd1d42288929d831e52" }, "source": [ "## Tree Ensemble Modelling" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "4f676f954b387a3ac74769def862d399d114334c" }, "source": [ "Let's start by defining the evaluation criteria (quadratic weighted kappa). We will use the sci-kit learn's `cohen_kappa_score` function for cross-validation." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "_uuid": "85e97e601629f830b805cca4c3cbd6b078eaf49e", "collapsed": true }, "outputs": [], "source": [ "def metric(y1,y2):\n", " return cohen_kappa_score(y1, y2, weights = 'quadratic')\n", "\n", "# Make scorer for scikit-learn\n", "scorer = make_scorer(metric)" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "48a914b48275f63d0db3d52af0d1f274adf9e147" }, "source": [ "Models: \n", "- XGBoost (xgboost.sklearn)\n", "- Adaboost (sklearn.ensemble.AdaBoostClassifier)\n", "- Random Forest (sklearn.ensemble.RandomForestClassifier)\n", "- Gradient Boosted (sklearn.ensembl.GradientBoostingClassifier)\n", "- Extra Trees (sklearn.ensembl.ExtraTreesClassifier)\n" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "_kg_hide-input": false, "_uuid": "0cf7e9a749ebf80311b19c235c39cbbede630f28", "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 3 folds for each of 16 candidates, totalling 48 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 48 out of 48 | elapsed: 4.5min finished\n" ] }, { "data": { "text/plain": [ "{'learning_rate': 0.1,\n", " 'loss': 'deviance',\n", " 'max_depth': 5,\n", " 'max_features': 'auto',\n", " 'min_samples_leaf': 15,\n", " 'min_samples_split': 5,\n", " 'n_estimators': 100,\n", " 'random_state': 1234,\n", " 'subsample': 0.9}" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gbm_model = GradientBoostingClassifier()\n", "gbm_grid = {\n", " 'loss' : ['deviance'],\n", " 'learning_rate' : [0.1],\n", " 'n_estimators' : [100],\n", " 'subsample' : [0.8, 0.9],\n", " 'min_samples_split' : [5,10],\n", " 'random_state' : [1234],\n", " 'max_depth' : [5, 7],\n", " 'max_features' : ['auto'],\n", " 'min_samples_leaf': [15,20]\n", "}\n", "\n", "gbm_gridsearch = GridSearchCV(estimator = gbm_model,\n", " param_grid = gbm_grid, \n", " cv = 3, \n", " n_jobs = -1, \n", " verbose = 1, \n", " scoring = scorer)\n", "\n", "gbm_gridsearch.fit(clean_train, train_target)\n", "gbm_gridsearch.best_params_" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "_uuid": "d68d7721edfba79274d465915d91290667c5e201", "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 12 out of 12 | elapsed: 7.8s finished\n" ] }, { "data": { "text/plain": [ "{'algorithm': 'SAMME.R',\n", " 'learning_rate': 0.35,\n", " 'n_estimators': 150,\n", " 'random_state': 1234}" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ada_model = AdaBoostClassifier()\n", "ada_grid = {\n", " 'random_state' : [1234],\n", " 'n_estimators' : [135, 150],\n", " 'learning_rate' : [0.35, 0.5],\n", " 'algorithm' : ['SAMME.R']\n", "}\n", "\n", "ada_gridsearch = GridSearchCV(estimator = ada_model,\n", " param_grid = ada_grid, \n", " cv = 3, \n", " n_jobs = -1, \n", " verbose = 1, \n", " scoring = scorer)\n", "\n", "ada_gridsearch.fit(clean_train, train_target)\n", "ada_gridsearch.best_params_" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "_uuid": "88d9331ebe591469ffcbc8b44c5728fdad61d4ce" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 3 folds for each of 144 candidates, totalling 432 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 16.3s\n", "[Parallel(n_jobs=-1)]: Done 192 tasks | elapsed: 1.3min\n", "[Parallel(n_jobs=-1)]: Done 432 out of 432 | elapsed: 3.0min finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0.2928920722473285\n" ] }, { "data": { "text/plain": [ "{'bootstrap': 'true',\n", " 'criterion': 'gini',\n", " 'max_depth': 50,\n", " 'max_features': 'auto',\n", " 'min_samples_leaf': 5,\n", " 'min_samples_split': 5,\n", " 'n_estimators': 300,\n", " 'random_state': 1234}" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ets_model = ExtraTreesClassifier()\n", "ets_grid = {\n", " 'random_state' : [1234],\n", " 'n_estimators' : [50, 150, 300],\n", " 'criterion' : ['gini'],\n", " 'max_depth' : [10, 25, 50],\n", " 'max_features' : ['auto'],\n", " 'min_samples_split' : [5, 10, 15, 30],\n", " 'min_samples_leaf' : [5, 10, 15, 30],\n", " 'bootstrap' : ['true']\n", "}\n", "\n", "ets_gridsearch = GridSearchCV(estimator = ets_model,\n", " param_grid = ets_grid, \n", " cv = 3, \n", " n_jobs = -1, \n", " verbose = 1, \n", " scoring = scorer)\n", "\n", "ets_gridsearch.fit(clean_train, train_target)\n", "print(ets_gridsearch.best_score_)\n", "ets_gridsearch.best_params_\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "_uuid": "0483f34c09386c74678826b01ac77af4b1f558e3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 3 folds for each of 36 candidates, totalling 108 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 54.8s\n", "[Parallel(n_jobs=-1)]: Done 108 out of 108 | elapsed: 2.3min finished\n" ] }, { "data": { "text/plain": [ "{'max_depth': 35,\n", " 'max_features': 'auto',\n", " 'min_samples_leaf': 5,\n", " 'min_samples_split': 15,\n", " 'n_estimators': 300,\n", " 'random_state': 1234}" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rfc_model = RandomForestClassifier()\n", "rfc_grid = {\n", " 'random_state' : [1234],\n", " 'n_estimators' : [250, 300, 350],\n", " 'max_depth' : [35, 50, 75],\n", " 'max_features' : ['auto'],\n", " 'min_samples_leaf': [5, 10],\n", " 'min_samples_split': [10, 15]\n", "}\n", "\n", "rfc_gridsearch = GridSearchCV(estimator = rfc_model,\n", " param_grid = rfc_grid, \n", " cv = 3, \n", " n_jobs = -1, \n", " verbose = 1, \n", " scoring = scorer)\n", "\n", "rfc_gridsearch.fit(clean_train, train_target)\n", "rfc_gridsearch.best_params_" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "_uuid": "045335e5950a4ec0ef74738ea3f62360e7c5bcb3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 3 folds for each of 36 candidates, totalling 108 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 8.3min\n", "[Parallel(n_jobs=-1)]: Done 108 out of 108 | elapsed: 21.3min finished\n" ] }, { "data": { "text/plain": [ "{'gamma': 0.7,\n", " 'learning_rate': 0.1,\n", " 'max_depth': 10,\n", " 'min_child_weight': 2,\n", " 'n_estimators': 150,\n", " 'random_state': 1234}" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xgb_model = XGBClassifier()\n", "xgb_grid = {\n", " 'n_estimators' : [150, 200],\n", " 'random_state' : [1234],\n", " 'max_depth': [10,12,15],\n", " 'min_child_weight': [2,3],\n", " 'learning_rate': [0.1],\n", " 'gamma': [0.6, 0.7, 0.8]\n", "}\n", "\n", "xgb_gridsearch = GridSearchCV(estimator = xgb_model,\n", " param_grid = xgb_grid, \n", " cv = 3, \n", " n_jobs = -1, \n", " verbose = 1, \n", " scoring = scorer)\n", "\n", "xgb_gridsearch.fit(clean_train, train_target)\n", "xgb_gridsearch.best_params_" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "_uuid": "93be5ccd76c1128a721592b24e5a4936e798f3c0" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best Model Parameters: \n", "\n", "AdaBoost Classifier Score: 0.3252\n", "{'algorithm': 'SAMME.R', 'learning_rate': 0.35, 'n_estimators': 150, 'random_state': 1234}\n", "\n", "GradientBoosting Classifier Score: 0.3538\n", "{'learning_rate': 0.1, 'loss': 'deviance', 'max_depth': 5, 'max_features': 'auto', 'min_samples_leaf': 15, 'min_samples_split': 5, 'n_estimators': 100, 'random_state': 1234, 'subsample': 0.9}\n", "\n", "Extra Trees Classifier Score: 0.2929\n", "{'bootstrap': 'true', 'criterion': 'gini', 'max_depth': 50, 'max_features': 'auto', 'min_samples_leaf': 5, 'min_samples_split': 5, 'n_estimators': 300, 'random_state': 1234}\n", "\n", "Random Forest Classifier Score: 0.3449\n", "{'max_depth': 35, 'max_features': 'auto', 'min_samples_leaf': 5, 'min_samples_split': 15, 'n_estimators': 300, 'random_state': 1234}\n", "\n", "XGBoost Classifier Score: 0.3526\n", "{'gamma': 0.7, 'learning_rate': 0.1, 'max_depth': 10, 'min_child_weight': 2, 'n_estimators': 150, 'random_state': 1234}\n", "\n" ] } ], "source": [ "print(\"Best Model Parameters: \\n\")\n", "\n", "print(\"AdaBoost Classifier Score: {:.4f}\\n{}\\n\".format(ada_gridsearch.best_score_, ada_gridsearch.best_params_))\n", "print(\"GradientBoosting Classifier Score: {:.4f}\\n{}\\n\".format(gbm_gridsearch.best_score_, gbm_gridsearch.best_params_))\n", "print(\"Extra Trees Classifier Score: {:.4f}\\n{}\\n\".format(ets_gridsearch.best_score_, ets_gridsearch.best_params_))\n", "print(\"Random Forest Classifier Score: {:.4f}\\n{}\\n\".format(rfc_gridsearch.best_score_, rfc_gridsearch.best_params_))\n", "print(\"XGBoost Classifier Score: {:.4f}\\n{}\\n\".format(xgb_gridsearch.best_score_, xgb_gridsearch.best_params_))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "53ab23fbdfcc54d61defe2bf7b39afa7c972b630" }, "source": [ "## Predictions & Submission" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "_uuid": "fac7a1e0ed03db5e2be0097f6ab53cdfd5cae460", "collapsed": true }, "outputs": [], "source": [ "pred1 = ada_gridsearch.predict(clean_test)\n", "pred2 = gbm_gridsearch.predict(clean_test)\n", "#pred3 = ets_gridsearch.predict(clean_test) # Did not perform well\n", "pred4 = rfc_gridsearch.predict(clean_test)\n", "pred5 = xgb_gridsearch.predict(clean_test)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "_uuid": "22d8dff58fa7f3a8f34212fcf979b12cafb62ee2" }, "outputs": [ { "data": { "text/html": [ "
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AdaBoostGradientBoostingRandom ForestXGBoostAverage
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" ], "text/plain": [ " AdaBoost GradientBoosting Random Forest XGBoost Average\n", "0 2 2 2 2 2\n", "1 4 4 4 4 4\n", "2 4 4 4 4 4\n", "3 4 4 4 3 4\n", "4 4 4 4 4 4" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_preds = pd.DataFrame(data=[pred1, pred2, pred4, pred5]).transpose()\n", "final_preds.columns = (['AdaBoost', 'GradientBoosting', 'Random Forest', 'XGBoost'])\n", "final_preds['Average'] = round(final_preds.mean(axis=1)).astype(int)\n", "final_preds.head()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "_uuid": "70822896218cd109ce4f8c226eed52e332723ec6", "collapsed": true }, "outputs": [], "source": [ "submission_df = pd.DataFrame(data = {'PetID' : test['PetID'], \n", " 'AdoptionSpeed' : final_preds.Average})\n", "submission_df.to_csv('submission.csv', index = False)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "_uuid": "fc71ebfab8a78bccd628da5a48dff958f47b364f" }, "outputs": [ { "data": { "text/html": [ "
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PetIDAdoptionSpeed
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" ], "text/plain": [ " PetID AdoptionSpeed\n", "0 378fcc4fc 2\n", "1 73c10e136 4\n", "2 72000c4c5 4" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "submission_df.head(3)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 1 }