02-Conversion_rate_challenge_Kamlesh_completed.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img 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\" alt=\"DSW LOGO\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0eiKSLYG8XvO"
   },
   "source": [
    "# Challenge : predict conversions 🏆🏆\n",
    "\n",
    "This is the template that shows the different steps of the challenge. In this notebook, all the training/predictions steps are implemented for a very basic model (logistic regression with only one variable). Please use this template and feel free to change the preprocessing/training steps to get the model with the best f1-score ! May the force be with you 🧨🧨  \n",
    "\n",
    "**For a detailed description of this project, please refer to *02-Conversion_rate_challenge.ipynb*.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Install plotly and import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AGhdl7Bt2xZd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: plotly in /opt/conda/lib/python3.8/site-packages (5.3.1)\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.8/site-packages (from plotly) (1.15.0)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in /opt/conda/lib/python3.8/site-packages (from plotly) (8.0.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import f1_score, confusion_matrix\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder, StandardScaler, LabelEncoder\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import plotly.io as pio\n",
    "# setting Jedha color palette as default\n",
    "pio.templates[\"jedha\"] = go.layout.Template(\n",
    "    layout_colorway=[\"#4B9AC7\", \"#4BE8E0\", \"#9DD4F3\", \"#97FBF6\", \"#2A7FAF\", \"#23B1AB\", \"#0E3449\", \"#015955\"]\n",
    ")\n",
    "pio.templates.default = \"jedha\"\n",
    "pio.renderers.default = \"iframe_connected\" # to be replaced by \"iframe\" if working on JULIE\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LHgro65rxKF7"
   },
   "source": [
    "# Read file with labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "W1AU8AH8u0qd",
    "outputId": "00698a97-027b-493b-a2e4-33fdcc295abb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set with labels (our train+test) : (284580, 6)\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('conversion_data_train.csv')\n",
    "print('Set with labels (our train+test) :', data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>country</th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>source</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>China</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>Direct</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UK</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Ads</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Germany</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>Seo</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Direct</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  age  new_user  source  total_pages_visited  converted\n",
       "0    China   22         1  Direct                    2          0\n",
       "1       UK   21         1     Ads                    3          0\n",
       "2  Germany   20         0     Seo                   14          1\n",
       "3       US   23         1     Seo                    3          0\n",
       "4       US   28         1  Direct                    3          0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0XwjKBc63B1n"
   },
   "source": [
    "# Explore dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NM0feCss5sLZ"
   },
   "outputs": [],
   "source": [
    "# The dataset is quite big : you must create a sample of the dataset before making any visualizations !\n",
    "data_sample = data.sample(10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>country</th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>source</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>115296</th>\n",
       "      <td>UK</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Direct</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194496</th>\n",
       "      <td>China</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>Direct</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113367</th>\n",
       "      <td>China</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>Seo</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227649</th>\n",
       "      <td>US</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10441</th>\n",
       "      <td>US</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>Direct</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  age  new_user  source  total_pages_visited  converted\n",
       "115296      UK   28         1  Direct                    1          0\n",
       "194496   China   17         0  Direct                    3          0\n",
       "113367   China   43         0     Seo                    8          0\n",
       "227649      US   27         1     Seo                    8          0\n",
       "10441       US   23         1  Direct                    5          0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 6)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sample.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Country distribution of our customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe\n",
       "    scrolling=\"no\"\n",
       "    width=\"100%\"\n",
       "    height=\"545px\"\n",
       "    src=\"iframe_figures/figure_8.html\"\n",
       "    frameborder=\"0\"\n",
       "    allowfullscreen\n",
       "></iframe>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(data_sample, x='country')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe\n",
       "    scrolling=\"no\"\n",
       "    width=\"100%\"\n",
       "    height=\"545px\"\n",
       "    src=\"iframe_figures/figure_9.html\"\n",
       "    frameborder=\"0\"\n",
       "    allowfullscreen\n",
       "></iframe>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(data_sample, x='source')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe\n",
       "    scrolling=\"no\"\n",
       "    width=\"100%\"\n",
       "    height=\"545px\"\n",
       "    src=\"iframe_figures/figure_10.html\"\n",
       "    frameborder=\"0\"\n",
       "    allowfullscreen\n",
       "></iframe>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(data_sample, x='country', y='total_pages_visited')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe\n",
       "    scrolling=\"no\"\n",
       "    width=\"100%\"\n",
       "    height=\"545px\"\n",
       "    src=\"iframe_figures/figure_11.html\"\n",
       "    frameborder=\"0\"\n",
       "    allowfullscreen\n",
       "></iframe>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(data_sample, x='total_pages_visited', y='converted')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "70MwsoCS3QD5"
   },
   "source": [
    "# Make your model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dPh1qPTf3wZU"
   },
   "source": [
    "## Choose variables to use in the model, and create train and test sets\n",
    "**From the EDA, we know that the most useful feature is total_pages_visited. Let's create a baseline model by using at first only this feature : in the next cells, we'll make preprocessings and train a simple (univariate) logistic regression.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sjEHMGoY3kMB"
   },
   "outputs": [],
   "source": [
    "features_list = ['total_pages_visited','country', 'age', 'new_user', 'source']\n",
    "numeric_indices = [0, 2]\n",
    "categorical_indices = [1, 3, 4]\n",
    "target_variable = 'converted'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "SV5E9KMs4xcq",
    "outputId": "9d1ed76e-e82e-45e7-f3e5-6d47962caa5a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explanatory variables :  Index(['total_pages_visited', 'country', 'age', 'new_user', 'source'], dtype='object')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = data.loc[:, features_list]\n",
    "Y = data.loc[:, target_variable]\n",
    "\n",
    "print('Explanatory variables : ', X.columns)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "id": "W8K5DQEvvQgl",
    "outputId": "d280ebc9-4d4b-4723-b9fe-32513f898abc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dividing into train and test sets...\n",
      "...Done.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Divide dataset Train set & Test set \n",
    "print(\"Dividing into train and test sets...\")\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, random_state=0)\n",
    "print(\"...Done.\")\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 218
    },
    "colab_type": "code",
    "id": "vVu0eXQD4xVc",
    "outputId": "83a5f553-f50d-44dc-d12a-6cb21e74e4d7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convert pandas DataFrames to numpy arrays...\n",
      "...Done\n",
      "[[4 'China' 23 0 'Direct']\n",
      " [8 'US' 25 1 'Direct']\n",
      " [2 'US' 32 1 'Seo']\n",
      " [3 'US' 37 1 'Seo']\n",
      " [4 'UK' 24 1 'Direct']]\n",
      "[[7 'US' 20 1 'Ads']\n",
      " [5 'US' 31 1 'Seo']]\n",
      "\n",
      "[0 0 0 0 0]\n",
      "[0 0]\n"
     ]
    }
   ],
   "source": [
    "# Convert pandas DataFrames to numpy arrays before using scikit-learn\n",
    "print(\"Convert pandas DataFrames to numpy arrays...\")\n",
    "X_train = X_train.values\n",
    "X_test = X_test.values\n",
    "Y_train = Y_train.values\n",
    "Y_test = Y_test.values\n",
    "print(\"...Done\")\n",
    "\n",
    "print(X_train[0:5,:])\n",
    "print(X_test[0:2,:])\n",
    "print()\n",
    "print(Y_train[0:5])\n",
    "print(Y_test[0:2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7b_aU7ij7K3Q"
   },
   "source": [
    "## Training pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "colab_type": "code",
    "id": "_9bEZ5bn7I5Z",
    "outputId": "ad5c8f97-2d25-4827-f1ee-43c665a97fa0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoding categorical features and standardizing numerical features...\n",
      "...Done\n",
      "[[-0.26070136 -0.91516278  0.          0.          0.          0.\n",
      "   1.          0.        ]\n",
      " [ 0.93728655 -0.67320988  0.          0.          1.          1.\n",
      "   1.          0.        ]\n",
      " [-0.85969532  0.17362526  0.          0.          1.          1.\n",
      "   0.          1.        ]\n",
      " [-0.56019834  0.7785075   0.          0.          1.          1.\n",
      "   0.          1.        ]\n",
      " [-0.26070136 -0.79418633  0.          1.          0.          1.\n",
      "   1.          0.        ]]\n"
     ]
    }
   ],
   "source": [
    "# Put here all the preprocessings\n",
    "print(\"Encoding categorical features and standardizing numerical features...\")\n",
    "\n",
    "# Create pipeline for numeric features\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='mean')), # missing values in Age will be replaced by columns' mean\n",
    "    ('scaler', StandardScaler())\n",
    "])\n",
    "\n",
    "# Create pipeline for categorical features\n",
    "categorical_transformer = Pipeline(\n",
    "    steps=[\n",
    "    ('imputer', SimpleImputer(strategy='most_frequent')), # missing values will be replaced by most frequent value\n",
    "    ('encoder', OneHotEncoder(drop='first')) # first column will be dropped to avoid creating correlations between features\n",
    "    ])\n",
    "\n",
    "\n",
    "# Use ColumnTranformer to make a preprocessor object that describes all the treatments to be done\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_indices),\n",
    "        ('cat', categorical_transformer, categorical_indices)\n",
    "    ])\n",
    "\n",
    "X_train = preprocessor.fit_transform(X_train)\n",
    "print(\"...Done\")\n",
    "print(X_train[0:5,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104
    },
    "colab_type": "code",
    "id": "1qhidLbq7o-5",
    "outputId": "6bfb746c-1ff4-41c9-b0d6-a98fd09a444d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train model...\n",
      "...Done.\n"
     ]
    }
   ],
   "source": [
    "# Train model\n",
    "print(\"Train model...\")\n",
    "classifier = LogisticRegression() # \n",
    "classifier.fit(X_train, Y_train)\n",
    "print(\"...Done.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 84
    },
    "colab_type": "code",
    "id": "Au2TK_vw7rD-",
    "outputId": "702789a8-4631-4c29-f297-e4b2901f3195"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions on training set...\n",
      "...Done.\n",
      "[0 0 0 ... 0 0 0]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predictions on training set\n",
    "print(\"Predictions on training set...\")\n",
    "Y_train_pred = classifier.predict(X_train)\n",
    "print(\"...Done.\")\n",
    "print(Y_train_pred)\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7TY_v9uH_CE7"
   },
   "source": [
    "## Test pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 134
    },
    "colab_type": "code",
    "id": "ngOSdG6-_Cvb",
    "outputId": "1e19e8ee-222f-413b-9bc0-e9f41dcca1c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoding categorical features and standardizing numerical features...\n",
      "...Done\n",
      "[[ 0.63778957 -1.27809213  0.          0.          1.          1.\n",
      "   0.          0.        ]\n",
      " [ 0.03879562  0.05264881  0.          0.          1.          1.\n",
      "   0.          1.        ]\n",
      " [-0.26070136 -0.31028053  0.          0.          1.          0.\n",
      "   0.          1.        ]\n",
      " [-0.26070136 -0.67320988  1.          0.          0.          1.\n",
      "   0.          0.        ]\n",
      " [ 0.63778957  1.62534265  0.          0.          1.          0.\n",
      "   0.          0.        ]]\n"
     ]
    }
   ],
   "source": [
    "# Use X_test, and the same preprocessings as in training pipeline, \n",
    "# but call \"transform()\" instead of \"fit_transform\" methods (see example below)\n",
    "\n",
    "print(\"Encoding categorical features and standardizing numerical features...\")\n",
    "\n",
    "X_test = preprocessor.transform(X_test)\n",
    "print(\"...Done\")\n",
    "print(X_test[0:5,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 84
    },
    "colab_type": "code",
    "id": "QS1XrzzE_jQI",
    "outputId": "866a96d2-4180-4bd1-ce54-ba052e75d485"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions on test set...\n",
      "...Done.\n",
      "[0 0 0 ... 0 0 0]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predictions on test set\n",
    "print(\"Predictions on test set...\")\n",
    "Y_test_pred = classifier.predict(X_test)\n",
    "print(\"...Done.\")\n",
    "print(Y_test_pred)\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zxJCTlz0_2it"
   },
   "source": [
    "## Performance assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "6x7p1nyr_3UV",
    "outputId": "8e5b91ba-ca06-4486-d808-37a6aaaa8cf7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1-score on train set :  0.7639889569725944\n",
      "f1-score on test set :  0.7562828755113967\n"
     ]
    }
   ],
   "source": [
    "# WARNING : Use the same score as the one that will be used by Kaggle !\n",
    "# Here, the f1-score will be used to assess the performances on the leaderboard\n",
    "print(\"f1-score on train set : \", f1_score(Y_train, Y_train_pred))\n",
    "print(\"f1-score on test set : \", f1_score(Y_test, Y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 151
    },
    "colab_type": "code",
    "id": "KhDTCeBy__JK",
    "outputId": "72c82d66-d765-437e-e9ef-4ccc80e7183f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix on train set : \n",
      "[[246944    955]\n",
      " [  2550   5673]]\n",
      "\n",
      "Confusion matrix on test set : \n",
      "[[27394   107]\n",
      " [  310   647]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# You can also check more performance metrics to better understand what your model is doing\n",
    "print(\"Confusion matrix on train set : \")\n",
    "print(confusion_matrix(Y_train, Y_train_pred))\n",
    "print()\n",
    "print(\"Confusion matrix on test set : \")\n",
    "print(confusion_matrix(Y_test, Y_test_pred))\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Our baseline model reaches a f1-score of almost 70%. Now, feel free to refine your model and try to beat this score ! 🚀🚀**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6tVVDRABv91O"
   },
   "source": [
    "# Train best classifier on all data and use it to make predictions on X_without_labels\n",
    "**Before making predictions on the file conversion_data_test.csv, let's train our model on ALL the data that was in conversion_data_train.csv. Sometimes, this allows to make tiny improvements in the score because we're using more examples to train the model.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 154
    },
    "colab_type": "code",
    "id": "M14RHUadzE2p",
    "outputId": "abcfcfec-9461-4579-adbd-f23270f984eb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate our train and test set to train your best classifier on all data with labels\n",
    "X = np.append(X_train,X_test,axis=0)\n",
    "Y = np.append(Y_train,Y_test)\n",
    "\n",
    "classifier.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 151
    },
    "colab_type": "code",
    "id": "Tr4CEaPzzbP-",
    "outputId": "f0d1c8ed-be4b-4974-d7b9-f23a49344d9d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction set (without labels) : (31620, 5)\n",
      "Convert pandas DataFrames to numpy arrays...\n",
      "...Done\n",
      "[[16 'UK' 28 0 'Seo']\n",
      " [5 'UK' 22 1 'Direct']\n",
      " [1 'China' 32 1 'Seo']\n",
      " [6 'US' 32 1 'Ads']\n",
      " [3 'China' 25 0 'Seo']]\n"
     ]
    }
   ],
   "source": [
    "# Read data without labels\n",
    "data_without_labels = pd.read_csv('conversion_data_test.csv')\n",
    "print('Prediction set (without labels) :', data_without_labels.shape)\n",
    "\n",
    "# Warning : check consistency of features_list (must be the same than the features \n",
    "# used by your best classifier)\n",
    "features_list = ['total_pages_visited','country', 'age', 'new_user', 'source']\n",
    "X_without_labels = data_without_labels.loc[:, features_list]\n",
    "\n",
    "# Convert pandas DataFrames to numpy arrays before using scikit-learn\n",
    "print(\"Convert pandas DataFrames to numpy arrays...\")\n",
    "X_without_labels = X_without_labels.values\n",
    "print(\"...Done\")\n",
    "\n",
    "print(X_without_labels[0:5,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 134
    },
    "colab_type": "code",
    "id": "LoUISfsT0HMR",
    "outputId": "e42dc389-5e77-4e13-ccbc-1fef4aa2c0ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoding categorical features and standardizing numerical features...\n",
      "...Done\n",
      "[[ 3.33326238 -0.31028053  0.          1.          0.          0.\n",
      "   0.          1.        ]\n",
      " [ 0.03879562 -1.03613923  0.          1.          0.          1.\n",
      "   1.          0.        ]\n",
      " [-1.15919229  0.17362526  0.          0.          0.          1.\n",
      "   0.          1.        ]\n",
      " [ 0.3382926   0.17362526  0.          0.          1.          1.\n",
      "   0.          0.        ]\n",
      " [-0.56019834 -0.67320988  0.          0.          0.          0.\n",
      "   0.          1.        ]]\n"
     ]
    }
   ],
   "source": [
    "# WARNING : PUT HERE THE SAME PREPROCESSING AS FOR YOUR TEST SET\n",
    "# CHECK YOU ARE USING X_without_labels\n",
    "print(\"Encoding categorical features and standardizing numerical features...\")\n",
    "\n",
    "X_without_labels = preprocessor.transform(X_without_labels)\n",
    "print(\"...Done\")\n",
    "print(X_without_labels[0:5,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7DuWSEHuwEQJ"
   },
   "outputs": [],
   "source": [
    "# Make predictions and dump to file\n",
    "# WARNING : MAKE SURE THE FILE IS A CSV WITH ONE COLUMN NAMED 'converted' AND NO INDEX !\n",
    "# WARNING : FILE NAME MUST HAVE FORMAT 'conversion_data_test_predictions_[name].csv'\n",
    "# where [name] is the name of your team/model separated by a '-'\n",
    "# For example : [name] = AURELIE-model1\n",
    "data = {\n",
    "    'converted': classifier.predict(X_without_labels)\n",
    "}\n",
    "\n",
    "Y_predictions = pd.DataFrame(columns=['converted'],data=data)\n",
    "Y_predictions.to_csv('conversion_data_test_predictions_Kamlesh-model1.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing the coefficients and interpreting the result\n",
    "**In this template, we just trained a model with only one feature (total_pages_visited), so there's no analysis to be done about the feature importance 🤔**\n",
    "\n",
    "**Once you've included more features in your model, please take some time to analyze the model's parameters and try to find some lever for action to improve the newsletter's conversion rate 😎😎**"
   ]
  }
 ],
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