eda.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Door Dash Data Exploratory Data Analysis/Model Fitting\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook will 'explore' the dataset we are working with before making any assumptions. This will help guide my decisions when 'transforming' my raw formatted data into production level data so it can be stored into our data warehouse (SnowFlake).\n",
    "\n",
    "\n",
    "<b> Problem </b> : Lets assume for now this data is being used to create a machine learning model that can help predict whether or not the driver will receive a tip. I will gear my data transformation to cater to a machine learning dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'config' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\caryk\\Desktop\\MLpipe\\eda.ipynb Cell 3\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/caryk/Desktop/MLpipe/eda.ipynb#W2sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mpandas\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpd\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/caryk/Desktop/MLpipe/eda.ipynb#W2sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/caryk/Desktop/MLpipe/eda.ipynb#W2sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mutils\u001b[39;00m \u001b[39mimport\u001b[39;00m io\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/caryk/Desktop/MLpipe/eda.ipynb#W2sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/caryk/Desktop/MLpipe/eda.ipynb#W2sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mdatetime\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\caryk\\Desktop\\MLpipe\\utils\\io.py:12\u001b[0m, in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'config' is not defined"
     ]
    }
   ],
   "source": [
    "## Import common packages and \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from utils import io\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import seaborn as sns\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading our Data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets take a look at the raw data. We can take a look at we we are dealing with by first listing out all raw data files in our bucket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['raw/1.csv', 'raw/10.csv', 'raw/11.csv', 'raw/12.csv', 'raw/13.csv', 'raw/14.csv', 'raw/15.csv', 'raw/16.csv', 'raw/17.csv', 'raw/18.csv', 'raw/19.csv', 'raw/2.csv', 'raw/20.csv', 'raw/21.csv', 'raw/22.csv', 'raw/23.csv', 'raw/24.csv', 'raw/25.csv', 'raw/26.csv', 'raw/27.csv', 'raw/28.csv', 'raw/29.csv', 'raw/3.csv', 'raw/30.csv', 'raw/31.csv', 'raw/4.csv', 'raw/5.csv', 'raw/6.csv', 'raw/7.csv', 'raw/8.csv', 'raw/9.csv']\n"
     ]
    }
   ],
   "source": [
    "print(io.listfiles('raw'))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at the schema of these tables. We can import a csv file using the imp_file from the io module. We simply give it the directory of the file and the file name itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Loading in dataframe of day 1\n",
    "df = io.imp_file('raw','1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0.1                               int64\n",
       "Unnamed: 0                                 int64\n",
       "Customer_placed_order_datetime            object\n",
       "Placed_order_with_restaurant_datetime     object\n",
       "Driver_at_restaurant_datetime             object\n",
       "Delivered_to_consumer_datetime            object\n",
       "Driver_ID                                  int64\n",
       "Restaurant_ID                              int64\n",
       "Consumer_ID                                int64\n",
       "Is_New                                      bool\n",
       "Delivery_Region                           object\n",
       "Is_ASAP                                     bool\n",
       "Order_total                              float64\n",
       "Amount_of_discount                       float64\n",
       "Amount_of_tip                            float64\n",
       "Refunded_amount                          float64\n",
       "Total_time_elapsed                        object\n",
       "Day                                        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Schema\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0.1                             False\n",
       "Unnamed: 0                               False\n",
       "Customer_placed_order_datetime           False\n",
       "Placed_order_with_restaurant_datetime     True\n",
       "Driver_at_restaurant_datetime             True\n",
       "Delivered_to_consumer_datetime           False\n",
       "Driver_ID                                False\n",
       "Restaurant_ID                            False\n",
       "Consumer_ID                              False\n",
       "Is_New                                   False\n",
       "Delivery_Region                          False\n",
       "Is_ASAP                                  False\n",
       "Order_total                              False\n",
       "Amount_of_discount                       False\n",
       "Amount_of_tip                            False\n",
       "Refunded_amount                          False\n",
       "Total_time_elapsed                       False\n",
       "Day                                      False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking if there are any na values for columns\n",
    "df.isna().any()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0.1                               0\n",
       "Unnamed: 0                                 0\n",
       "Customer_placed_order_datetime             0\n",
       "Placed_order_with_restaurant_datetime      1\n",
       "Driver_at_restaurant_datetime            113\n",
       "Delivered_to_consumer_datetime             0\n",
       "Driver_ID                                  0\n",
       "Restaurant_ID                              0\n",
       "Consumer_ID                                0\n",
       "Is_New                                     0\n",
       "Delivery_Region                            0\n",
       "Is_ASAP                                    0\n",
       "Order_total                                0\n",
       "Amount_of_discount                         0\n",
       "Amount_of_tip                              0\n",
       "Refunded_amount                            0\n",
       "Total_time_elapsed                         0\n",
       "Day                                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be large amount of NaN values in the field 'Driver_at_restaurant_datetime'. However, since we will not be using this variable as one of our predictors I will simply drop the column. We can also remove a few the other fields that have no relevance to our goal (Indexes,IDs)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Transformation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets first remove irrelavent columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## DF version 1  Here we are simply dropping irrelavent columns\n",
    "v1 = df.drop(columns=['Customer_placed_order_datetime','Placed_order_with_restaurant_datetime','Unnamed: 0.1','Unnamed: 0','Driver_ID','Restaurant_ID','Consumer_ID','Driver_at_restaurant_datetime','Delivered_to_consumer_datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Order_total</th>\n",
       "      <th>Amount_of_discount</th>\n",
       "      <th>Amount_of_tip</th>\n",
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       "      <td>False</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>1</td>\n",
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       "      <th>558</th>\n",
       "      <td>True</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>True</td>\n",
       "      <td>20.03</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.64</td>\n",
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       "      <td>1</td>\n",
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       "      <td>True</td>\n",
       "      <td>Palo Alto</td>\n",
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       "      <td>6.0</td>\n",
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       "      <td>False</td>\n",
       "      <td>Palo Alto</td>\n",
       "      <td>False</td>\n",
       "      <td>56.51</td>\n",
       "      <td>6.0</td>\n",
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       "      <td>False</td>\n",
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       "<p>562 rows × 9 columns</p>\n",
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      "text/plain": [
       "     Is_New Delivery_Region  Is_ASAP  Order_total  Amount_of_discount  \\\n",
       "0     False       Palo Alto     True        40.80                 0.0   \n",
       "1     False       Palo Alto    False        34.66                 0.0   \n",
       "2     False       Palo Alto    False        15.79                 0.0   \n",
       "3     False       Palo Alto     True        23.35                 0.0   \n",
       "4     False       Palo Alto     True        36.99                 0.0   \n",
       "..      ...             ...      ...          ...                 ...   \n",
       "557   False        San Jose     True        22.31                 6.0   \n",
       "558    True        San Jose     True        20.03                 6.0   \n",
       "559    True       Palo Alto     True        15.73                 6.0   \n",
       "560   False       Palo Alto    False        56.51                 6.0   \n",
       "561   False       Palo Alto     True        16.33                 6.0   \n",
       "\n",
       "     Amount_of_tip  Refunded_amount         Total_time_elapsed  Day  \n",
       "0             6.12              0.0  0 days 00:56:01.000000000    1  \n",
       "1             3.95              0.0  0 days 00:59:57.000000000    1  \n",
       "2             0.79              0.0  0 days 01:52:35.000000000    1  \n",
       "3             1.59              0.0  0 days 01:06:36.000000000    1  \n",
       "4             2.85              0.0  0 days 00:49:08.000000000    1  \n",
       "..             ...              ...                        ...  ...  \n",
       "557           0.00              0.0  0 days 00:56:12.000000000    1  \n",
       "558           0.64              0.0  0 days 00:34:08.000000000    1  \n",
       "559           0.89              0.0  0 days 00:46:10.000000000    1  \n",
       "560           6.96              0.0  0 days 03:04:32.000000000    1  \n",
       "561           1.42              0.0  0 days 00:29:18.000000000    1  \n",
       "\n",
       "[562 rows x 9 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also add new fields to improve the performance of our machine learning model.\n",
    "\n",
    "1. First, lets notice that the `Total_time_elapsed` field is not quantitative. We would want to change the field type to float such that it represents the total amount of time taken in minutes\n",
    "\n",
    "2. Secondly, we have 3 categorical fields that can be One-Hot-Encoded `Is_New` , `Delivery_Region`, and `Is_ASAP`\n",
    "\n",
    "3. We need to create a new binary field for our response variable\n",
    "\n",
    "Lets do these three below:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Converting `Total_time_elapsed` to `Total Minutes`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_total_minutes(time_str):\n",
    "    # Split the string to get the hours, minutes, and seconds components\n",
    "    time_parts = time_str.split(' ')[-1].split(':')\n",
    "    hours = int(time_parts[0])\n",
    "    minutes = int(time_parts[1])\n",
    "    seconds = int(time_parts[2].split('.')[0])\n",
    "    microseconds = int(time_parts[2].split('.')[1])\n",
    "    \n",
    "    # Create a timedelta object representing the time difference\n",
    "    time_diff = datetime.timedelta(hours=hours, minutes=minutes, seconds=seconds, microseconds=microseconds)\n",
    "    \n",
    "    # Calculate the total number of minutes\n",
    "    total_minutes = time_diff.total_seconds() // 60\n",
    "    \n",
    "    return total_minutes\n",
    "# Convert the field 'Totol_time_elapsed' to total number of minues\n",
    "v1['Total Minutes'] = v1['Total_time_elapsed'].apply(get_total_minutes)\n",
    "\n",
    "# Lets drop the old column\n",
    "v2 = v1.drop(columns=['Total_time_elapsed'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### One hot encoding categorical fields (`Is_New`,`Delivery_Region`,`Is_ASAP`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "v3 = pd.get_dummies(v2, columns=['Is_New','Delivery_Region','Is_ASAP'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Adding our response variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order_total</th>\n",
       "      <th>Amount_of_discount</th>\n",
       "      <th>Refunded_amount</th>\n",
       "      <th>Day</th>\n",
       "      <th>Total Minutes</th>\n",
       "      <th>Is_New_False</th>\n",
       "      <th>Is_New_True</th>\n",
       "      <th>Delivery_Region_Mountain View</th>\n",
       "      <th>Delivery_Region_None</th>\n",
       "      <th>Delivery_Region_Palo Alto</th>\n",
       "      <th>Delivery_Region_San Jose</th>\n",
       "      <th>Is_ASAP_False</th>\n",
       "      <th>Is_ASAP_True</th>\n",
       "      <th>Tip?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.79</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>22.31</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>20.03</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>15.73</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>56.51</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>184.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>16.33</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>562 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Order_total  Amount_of_discount  Refunded_amount  Day  Total Minutes  \\\n",
       "0          40.80                 0.0              0.0    1           56.0   \n",
       "1          34.66                 0.0              0.0    1           59.0   \n",
       "2          15.79                 0.0              0.0    1          112.0   \n",
       "3          23.35                 0.0              0.0    1           66.0   \n",
       "4          36.99                 0.0              0.0    1           49.0   \n",
       "..           ...                 ...              ...  ...            ...   \n",
       "557        22.31                 6.0              0.0    1           56.0   \n",
       "558        20.03                 6.0              0.0    1           34.0   \n",
       "559        15.73                 6.0              0.0    1           46.0   \n",
       "560        56.51                 6.0              0.0    1          184.0   \n",
       "561        16.33                 6.0              0.0    1           29.0   \n",
       "\n",
       "     Is_New_False  Is_New_True  Delivery_Region_Mountain View  \\\n",
       "0               1            0                              0   \n",
       "1               1            0                              0   \n",
       "2               1            0                              0   \n",
       "3               1            0                              0   \n",
       "4               1            0                              0   \n",
       "..            ...          ...                            ...   \n",
       "557             1            0                              0   \n",
       "558             0            1                              0   \n",
       "559             0            1                              0   \n",
       "560             1            0                              0   \n",
       "561             1            0                              0   \n",
       "\n",
       "     Delivery_Region_None  Delivery_Region_Palo Alto  \\\n",
       "0                       0                          1   \n",
       "1                       0                          1   \n",
       "2                       0                          1   \n",
       "3                       0                          1   \n",
       "4                       0                          1   \n",
       "..                    ...                        ...   \n",
       "557                     0                          0   \n",
       "558                     0                          0   \n",
       "559                     0                          1   \n",
       "560                     0                          1   \n",
       "561                     0                          1   \n",
       "\n",
       "     Delivery_Region_San Jose  Is_ASAP_False  Is_ASAP_True  Tip?  \n",
       "0                           0              0             1     1  \n",
       "1                           0              1             0     1  \n",
       "2                           0              1             0     1  \n",
       "3                           0              0             1     1  \n",
       "4                           0              0             1     1  \n",
       "..                        ...            ...           ...   ...  \n",
       "557                         1              0             1     0  \n",
       "558                         1              0             1     1  \n",
       "559                         0              0             1     1  \n",
       "560                         0              1             0     1  \n",
       "561                         0              0             1     1  \n",
       "\n",
       "[562 rows x 14 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v3['Tip?'] = np.where(v3['Amount_of_tip'] == 0, 0, 1)\n",
    "finalv = v3.drop(columns=['Amount_of_tip'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Great!</b> Now we are ready to fit our model."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we fit our model, lets do some quick EDA.  Since we have done all our transformations, we can apply these transformations to the rest of the raw files in our s3 raw bucket. I have defined helper functions in the sibling directory `utils` that does the same transformations. I will import them and apply them to the rest of the raw files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing our helper functions\n",
    "import sys\n",
    "import os\n",
    "sys.path.append(os.path.abspath('../utils'))\n",
    "from utils import helpers\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below loops through the rest of the files (Days 2 - Days 31), transforms the dataframes into what the form I want, and concatenates the dataframes together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "current = finalv\n",
    "# Looping through files 2 to 31 and \n",
    "rangeofvalues = range(2,32)\n",
    "# Looping throug\n",
    "for i in rangeofvalues:\n",
    "    # Imports the ith file in the raw \n",
    "    df = io.imp_file('raw',str(i))\n",
    "    # Removes irrelevant columns\n",
    "    v1 =df.drop(columns=['Customer_placed_order_datetime','Placed_order_with_restaurant_datetime','Unnamed: 0.1','Unnamed: 0',\n",
    "                                   'Driver_ID','Restaurant_ID','Consumer_ID','Driver_at_restaurant_datetime','Delivered_to_consumer_datetime'])\n",
    "    # Adds the minute field\n",
    "    v2 = helpers.addminutefield(v1)\n",
    "    # One hot encodes the categorical fields\n",
    "    v3 = helpers.onehotencode(v2)\n",
    "    # Adds the response variables\n",
    "    v4 = helpers.addresponse(v3)\n",
    "\n",
    "\n",
    "    #Finally concatenate dataframes\n",
    "\n",
    "    current = pd.concat([current, v4])\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18078, 15)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Order_total', 'Amount_of_discount', 'Refunded_amount', 'Day',\n",
       "       'Total Minutes', 'Is_New_False', 'Is_New_True',\n",
       "       'Delivery_Region_Mountain View', 'Delivery_Region_None',\n",
       "       'Delivery_Region_Palo Alto', 'Delivery_Region_San Jose',\n",
       "       'Is_ASAP_False', 'Is_ASAP_True', 'Tip?', 'Amount_of_tip'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current.columns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great. Now we have a transformed data set we are able to work with. Lets visualize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Generate some random data\n",
    "data1 = current['Order_total']\n",
    "data2 = current['Amount_of_tip']\n",
    "data3 = current['Total Minutes']\n",
    "data4 = current['Refunded_amount']\n",
    "data5 = current['Amount_of_discount']\n",
    "\n",
    "\n",
    "# Create a figure with two subplots\n",
    "fig, axs = plt.subplots(3, 2,figsize = (30,20))\n",
    "\n",
    "# Plot the histograms in each subplot\n",
    "axs[0][0].hist(data1, bins=50, color='blue', alpha=0.5)\n",
    "axs[0][0].set_title('Order_total')\n",
    "axs[1][0].hist(data2, bins=50,color='green', alpha=0.5)\n",
    "axs[1][0].set_title('Amount_of_tip')\n",
    "axs[1][1].hist(data3,  bins=50,color='red', alpha=0.5)\n",
    "axs[1][1].set_title('Total Minutes')\n",
    "axs[0][1].hist(data4, bins=50, color='gray', alpha=0.5)\n",
    "axs[0][1].set_title('Refunded_amount')\n",
    "axs[2][0].hist(data5, bins=50, color='yellow', alpha=0.5)\n",
    "axs[2][0].set_title('Amount_of_discount')\n",
    "\n",
    "\n",
    "# Display the figure\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the distribution of all quantitative variables are right skewed.\n",
    "\n",
    "Lets also visualize the relationship between our independent and dependent variables. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Fitting"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are quite a few classifications methods in machine learning. In this notebook, we will only be taking a look at two. `Logistic regression` and `Random Forests`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets first split into test and training  \n",
    "from sklearn.model_selection import train_test_split  \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = current.drop(columns=['Tip?'])\n",
    "y = current['Tip?']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Order_total</th>\n",
       "      <th>Amount_of_discount</th>\n",
       "      <th>Refunded_amount</th>\n",
       "      <th>Day</th>\n",
       "      <th>Total Minutes</th>\n",
       "      <th>Is_New_False</th>\n",
       "      <th>Is_New_True</th>\n",
       "      <th>Delivery_Region_Mountain View</th>\n",
       "      <th>Delivery_Region_None</th>\n",
       "      <th>Delivery_Region_Palo Alto</th>\n",
       "      <th>Delivery_Region_San Jose</th>\n",
       "      <th>Is_ASAP_False</th>\n",
       "      <th>Is_ASAP_True</th>\n",
       "      <th>Amount_of_tip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.79</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>69.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31</td>\n",
       "      <td>131.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>102.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>96.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>56.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>8.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18078 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Order_total  Amount_of_discount  Refunded_amount  Day  Total Minutes  \\\n",
       "0         40.80                 0.0              0.0    1           56.0   \n",
       "1         34.66                 0.0              0.0    1           59.0   \n",
       "2         15.79                 0.0              0.0    1          112.0   \n",
       "3         23.35                 0.0              0.0    1           66.0   \n",
       "4         36.99                 0.0              0.0    1           49.0   \n",
       "..          ...                 ...              ...  ...            ...   \n",
       "50        69.95                 0.0              0.0   31          131.0   \n",
       "51       102.57                 0.0              0.0   31           41.0   \n",
       "52        96.26                 0.0              0.0   31           98.0   \n",
       "53        56.30                 0.0              0.0   31           56.0   \n",
       "54         8.69                 0.0              0.0   31           27.0   \n",
       "\n",
       "    Is_New_False  Is_New_True  Delivery_Region_Mountain View  \\\n",
       "0              1            0                              0   \n",
       "1              1            0                              0   \n",
       "2              1            0                              0   \n",
       "3              1            0                              0   \n",
       "4              1            0                              0   \n",
       "..           ...          ...                            ...   \n",
       "50             1            0                              1   \n",
       "51             1            0                              0   \n",
       "52             1            0                              0   \n",
       "53             1            0                              0   \n",
       "54             1            0                              1   \n",
       "\n",
       "    Delivery_Region_None  Delivery_Region_Palo Alto  Delivery_Region_San Jose  \\\n",
       "0                    0.0                          1                         0   \n",
       "1                    0.0                          1                         0   \n",
       "2                    0.0                          1                         0   \n",
       "3                    0.0                          1                         0   \n",
       "4                    0.0                          1                         0   \n",
       "..                   ...                        ...                       ...   \n",
       "50                   NaN                          0                         0   \n",
       "51                   NaN                          1                         0   \n",
       "52                   NaN                          1                         0   \n",
       "53                   NaN                          0                         1   \n",
       "54                   NaN                          0                         0   \n",
       "\n",
       "    Is_ASAP_False  Is_ASAP_True  Amount_of_tip  \n",
       "0               0             1            NaN  \n",
       "1               1             0            NaN  \n",
       "2               1             0            NaN  \n",
       "3               0             1            NaN  \n",
       "4               0             1            NaN  \n",
       "..            ...           ...            ...  \n",
       "50              1             0           2.94  \n",
       "51              0             1          13.32  \n",
       "52              1             0           8.30  \n",
       "53              0             1           8.45  \n",
       "54              0             1           0.87  \n",
       "\n",
       "[18078 rows x 14 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Logistic Regression`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\caryk\\Desktop\\MLpipe\\eda.ipynb Cell 44\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/caryk/Desktop/MLpipe/eda.ipynb#X44sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# Fitting our model\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/caryk/Desktop/MLpipe/eda.ipynb#X44sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m model \u001b[39m=\u001b[39m LogisticRegression()\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/caryk/Desktop/MLpipe/eda.ipynb#X44sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m model\u001b[39m.\u001b[39;49mfit(X_train, y_train)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/caryk/Desktop/MLpipe/eda.ipynb#X44sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m y_pred \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mpredict(X_test)\n",
      "File \u001b[1;32mc:\\Users\\caryk\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1508\u001b[0m, in \u001b[0;36mLogisticRegression.fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m   1505\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m   1506\u001b[0m     _dtype \u001b[39m=\u001b[39m [np\u001b[39m.\u001b[39mfloat64, np\u001b[39m.\u001b[39mfloat32]\n\u001b[1;32m-> 1508\u001b[0m X, y \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_data(\n\u001b[0;32m   1509\u001b[0m     X,\n\u001b[0;32m   1510\u001b[0m     y,\n\u001b[0;32m   1511\u001b[0m     accept_sparse\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mcsr\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m   1512\u001b[0m     dtype\u001b[39m=\u001b[39;49m_dtype,\n\u001b[0;32m   1513\u001b[0m     order\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mC\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m   1514\u001b[0m     accept_large_sparse\u001b[39m=\u001b[39;49msolver \u001b[39mnot\u001b[39;49;00m \u001b[39min\u001b[39;49;00m [\u001b[39m\"\u001b[39;49m\u001b[39mliblinear\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39msag\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39msaga\u001b[39;49m\u001b[39m\"\u001b[39;49m],\n\u001b[0;32m   1515\u001b[0m )\n\u001b[0;32m   1516\u001b[0m check_classification_targets(y)\n\u001b[0;32m   1517\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mclasses_ \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39munique(y)\n",
      "File \u001b[1;32mc:\\Users\\caryk\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:581\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    579\u001b[0m         y \u001b[39m=\u001b[39m check_array(y, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_y_params)\n\u001b[0;32m    580\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 581\u001b[0m         X, y \u001b[39m=\u001b[39m check_X_y(X, y, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_params)\n\u001b[0;32m    582\u001b[0m     out \u001b[39m=\u001b[39m X, y\n\u001b[0;32m    584\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m no_val_X \u001b[39mand\u001b[39;00m check_params\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mensure_2d\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mTrue\u001b[39;00m):\n",
      "File \u001b[1;32mc:\\Users\\caryk\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:964\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m    961\u001b[0m \u001b[39mif\u001b[39;00m y \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    962\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39my cannot be None\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m--> 964\u001b[0m X \u001b[39m=\u001b[39m check_array(\n\u001b[0;32m    965\u001b[0m     X,\n\u001b[0;32m    966\u001b[0m     accept_sparse\u001b[39m=\u001b[39;49maccept_sparse,\n\u001b[0;32m    967\u001b[0m     accept_large_sparse\u001b[39m=\u001b[39;49maccept_large_sparse,\n\u001b[0;32m    968\u001b[0m     dtype\u001b[39m=\u001b[39;49mdtype,\n\u001b[0;32m    969\u001b[0m     order\u001b[39m=\u001b[39;49morder,\n\u001b[0;32m    970\u001b[0m     copy\u001b[39m=\u001b[39;49mcopy,\n\u001b[0;32m    971\u001b[0m     force_all_finite\u001b[39m=\u001b[39;49mforce_all_finite,\n\u001b[0;32m    972\u001b[0m     ensure_2d\u001b[39m=\u001b[39;49mensure_2d,\n\u001b[0;32m    973\u001b[0m     allow_nd\u001b[39m=\u001b[39;49mallow_nd,\n\u001b[0;32m    974\u001b[0m     ensure_min_samples\u001b[39m=\u001b[39;49mensure_min_samples,\n\u001b[0;32m    975\u001b[0m     ensure_min_features\u001b[39m=\u001b[39;49mensure_min_features,\n\u001b[0;32m    976\u001b[0m     estimator\u001b[39m=\u001b[39;49mestimator,\n\u001b[0;32m    977\u001b[0m )\n\u001b[0;32m    979\u001b[0m y \u001b[39m=\u001b[39m _check_y(y, multi_output\u001b[39m=\u001b[39mmulti_output, y_numeric\u001b[39m=\u001b[39my_numeric)\n\u001b[0;32m    981\u001b[0m check_consistent_length(X, y)\n",
      "File \u001b[1;32mc:\\Users\\caryk\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:800\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    794\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    795\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mFound array with dim \u001b[39m\u001b[39m%d\u001b[39;00m\u001b[39m. \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m expected <= 2.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    796\u001b[0m             \u001b[39m%\u001b[39m (array\u001b[39m.\u001b[39mndim, estimator_name)\n\u001b[0;32m    797\u001b[0m         )\n\u001b[0;32m    799\u001b[0m     \u001b[39mif\u001b[39;00m force_all_finite:\n\u001b[1;32m--> 800\u001b[0m         _assert_all_finite(array, allow_nan\u001b[39m=\u001b[39;49mforce_all_finite \u001b[39m==\u001b[39;49m \u001b[39m\"\u001b[39;49m\u001b[39mallow-nan\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m    802\u001b[0m \u001b[39mif\u001b[39;00m ensure_min_samples \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m    803\u001b[0m     n_samples \u001b[39m=\u001b[39m _num_samples(array)\n",
      "File \u001b[1;32mc:\\Users\\caryk\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:114\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m    107\u001b[0m     \u001b[39mif\u001b[39;00m (\n\u001b[0;32m    108\u001b[0m         allow_nan\n\u001b[0;32m    109\u001b[0m         \u001b[39mand\u001b[39;00m np\u001b[39m.\u001b[39misinf(X)\u001b[39m.\u001b[39many()\n\u001b[0;32m    110\u001b[0m         \u001b[39mor\u001b[39;00m \u001b[39mnot\u001b[39;00m allow_nan\n\u001b[0;32m    111\u001b[0m         \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m np\u001b[39m.\u001b[39misfinite(X)\u001b[39m.\u001b[39mall()\n\u001b[0;32m    112\u001b[0m     ):\n\u001b[0;32m    113\u001b[0m         type_err \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39minfinity\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m allow_nan \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mNaN, infinity\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m--> 114\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    115\u001b[0m             msg_err\u001b[39m.\u001b[39mformat(\n\u001b[0;32m    116\u001b[0m                 type_err, msg_dtype \u001b[39mif\u001b[39;00m msg_dtype \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m X\u001b[39m.\u001b[39mdtype\n\u001b[0;32m    117\u001b[0m             )\n\u001b[0;32m    118\u001b[0m         )\n\u001b[0;32m    119\u001b[0m \u001b[39m# for object dtype data, we only check for NaNs (GH-13254)\u001b[39;00m\n\u001b[0;32m    120\u001b[0m \u001b[39melif\u001b[39;00m X\u001b[39m.\u001b[39mdtype \u001b[39m==\u001b[39m np\u001b[39m.\u001b[39mdtype(\u001b[39m\"\u001b[39m\u001b[39mobject\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m allow_nan:\n",
      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
     ]
    }
   ],
   "source": [
    "# Fitting our model\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\caryk\\Desktop\\MLpipe\\eda.ipynb Cell 46\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/caryk/Desktop/MLpipe/eda.ipynb#Y113sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m plot_confusion_matrix(model, X_test, y_test)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'model' is not defined"
     ]
    }
   ],
   "source": [
    "plot_confusion_matrix(model, X_test, y_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Random Forests`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred = rf.predict(X_test)\n",
    "accuracy = rf.score(X_test, y_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "e\n",
    "\n",
    "\n",
    "\n"
   ]
  }
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