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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open in Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/justmarkham/scikit-learn-tips/master?filepath=notebooks%2F50_simple_ml_pattern.ipynb)\n",
    "\n",
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/justmarkham/scikit-learn-tips/blob/master/notebooks/50_simple_ml_pattern.ipynb)\n",
    "\n",
    "# 🤖⚡ scikit-learn tip #50 ([video](https://www.youtube.com/watch?v=gd-TZut-oto&list=PL5-da3qGB5ID7YYAqireYEew2mWVvgmj6&index=50))\n",
    "\n",
    "Here's a simple pattern that can be adapted to solve many ML problems. It has plenty of shortcomings, but can work surprisingly well as-is!\n",
    "\n",
    "Check it out 👇\n",
    "\n",
    "Shortcomings include:\n",
    "\n",
    "- Assumes all columns have proper data types\n",
    "- May include irrelevant or improper features\n",
    "- Does not handle text or date columns well\n",
    "- Does not include feature engineering\n",
    "- Ordinal encoding may be better\n",
    "- Other imputation strategies may be better\n",
    "- Numeric features may not need scaling\n",
    "- A different model may be better\n",
    "- And so on..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('http://bit.ly/kaggletrain')\n",
    "X = df[cols]\n",
    "y = df['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = pd.read_csv('http://bit.ly/kaggletest', nrows=10)\n",
    "X_new = df_new[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.compose import make_column_selector, make_column_transformer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up preprocessing for numeric columns\n",
    "imp_median = SimpleImputer(strategy='median', add_indicator=True)\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up preprocessing for categorical columns\n",
    "imp_constant = SimpleImputer(strategy='constant')\n",
    "ohe = OneHotEncoder(handle_unknown='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select columns by data type\n",
    "num_cols = make_column_selector(dtype_include='number')\n",
    "cat_cols = make_column_selector(dtype_exclude='number')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# do all preprocessing\n",
    "preprocessor = make_column_transformer(\n",
    "    (make_pipeline(imp_median, scaler), num_cols),\n",
    "    (make_pipeline(imp_constant, ohe), cat_cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a pipeline\n",
    "pipe = make_pipeline(preprocessor, LogisticRegression())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8035904839620865"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cross-validate the pipeline\n",
    "cross_val_score(pipe, X, y).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 0, 1, 0, 1, 0])"
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the pipeline and make predictions\n",
    "pipe.fit(X, y)\n",
    "pipe.predict(X_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Want more tips? [View all tips on GitHub](https://github.com/justmarkham/scikit-learn-tips) or [Sign up to receive 2 tips by email every week](https://scikit-learn.tips) 💌\n",
    "\n",
    "© 2020 [Data School](https://www.dataschool.io). All rights reserved."
   ]
  }
 ],
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