ML-For-Beginners/Regression/4-Logistic/solution/notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression - Lesson 4\n",
    "\n",
    "Load up required libraries and dataset. Convert the data to a dataframe containing a subset of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City Name</th>\n",
       "      <th>Type</th>\n",
       "      <th>Package</th>\n",
       "      <th>Variety</th>\n",
       "      <th>Sub Variety</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Date</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Mostly Low</th>\n",
       "      <th>...</th>\n",
       "      <th>Unit of Sale</th>\n",
       "      <th>Quality</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Appearance</th>\n",
       "      <th>Storage</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Repack</th>\n",
       "      <th>Trans Mode</th>\n",
       "      <th>Unnamed: 24</th>\n",
       "      <th>Unnamed: 25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24 inch bins</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4/29/17</td>\n",
       "      <td>270.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24 inch bins</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5/6/17</td>\n",
       "      <td>270.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24 inch bins</td>\n",
       "      <td>HOWDEN TYPE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9/24/16</td>\n",
       "      <td>160.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24 inch bins</td>\n",
       "      <td>HOWDEN TYPE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9/24/16</td>\n",
       "      <td>160.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24 inch bins</td>\n",
       "      <td>HOWDEN TYPE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11/5/16</td>\n",
       "      <td>90.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
       "\n",
       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
       "\n",
       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
    "\n",
    "pumpkins.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "new_columns = ['Color','Origin','Item Size','Variety','City Name','Package']\n",
    "\n",
    "new_pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n",
    "\n",
    "new_pumpkins.dropna(inplace=True)\n",
    "\n",
    "new_pumpkins = new_pumpkins.apply(LabelEncoder().fit_transform)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the data shape, size, and quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of       City Name  Package  Variety  Origin  Item Size  Color\n",
       "2             1        3        4       3          3      0\n",
       "3             1        3        4      17          3      0\n",
       "4             1        3        4       5          2      0\n",
       "5             1        3        4       5          2      0\n",
       "6             1        4        4       5          3      0\n",
       "...         ...      ...      ...     ...        ...    ...\n",
       "1694         12        3        5       4          6      1\n",
       "1695         12        3        5       4          6      1\n",
       "1696         12        3        5       4          6      1\n",
       "1697         12        3        5       4          6      1\n",
       "1698         12        3        5       4          6      1\n",
       "\n",
       "[991 rows x 6 columns]>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pumpkins.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Working with Item Size to Color, create a scatterplot using Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f16fe681f90>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "g = sns.PairGrid(new_pumpkins)\n",
    "g.map(sns.scatterplot)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning: 80.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/conda/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning: 37.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Color', ylabel='Item Size'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(x=\"Color\", y=\"Item Size\", data=new_pumpkins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f16f4fe2d10>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Color\", y=\"Item Size\",\n",
    "            kind=\"violin\", data=new_pumpkins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "Selected_features = ['Origin','Item Size','Variety','City Name','Package']\n",
    "\n",
    "X = new_pumpkins[Selected_features]\n",
    "y = new_pumpkins['Color']\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.98      0.90       166\n",
      "           1       0.00      0.00      0.00        33\n",
      "\n",
      "    accuracy                           0.81       199\n",
      "   macro avg       0.42      0.49      0.45       199\n",
      "weighted avg       0.69      0.81      0.75       199\n",
      "\n",
      "Predicted labels:  [0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 1 0 0 0 0 0 0 0 0]\n",
      "Accuracy:  0.8140703517587939\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, predictions))\n",
    "print('Predicted labels: ', predictions)\n",
    "print('Accuracy: ', accuracy_score(y_test, predictions))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[162,   4],\n",
       "       [ 33,   0]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "y_scores = model.predict_proba(X_test)\n",
    "# calculate ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
    "sns.lineplot([0, 1], [0, 1])\n",
    "sns.lineplot(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6976998904709748\n"
     ]
    }
   ],
   "source": [
    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
    "print(auc)"
   ]
  }
 ],
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