diff --git a/Notebooks/modelling.ipynb b/Notebooks/modelling.ipynb new file mode 100644 index 0000000..c5282b5 --- /dev/null +++ b/Notebooks/modelling.ipynb @@ -0,0 +1,1161 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Importing Pandas an Numpy Libraries to use on manipulating our Data\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# To Preproccesing our data\n", + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "# To fill missing values\n", + "from sklearn.impute import SimpleImputer\n", + "\n", + "# To Split our train data\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# To Visualize Data\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# To Train our data\n", + "from xgboost import XGBClassifier\n", + "from sklearn.utils import shuffle\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.naive_bayes import BernoulliNB, GaussianNB\n", + "\n", + "# To evaluate end result we have \n", + "from sklearn.metrics import accuracy_score, confusion_matrix,mean_squared_error, r2_score, mean_absolute_error\n", + "from sklearn.model_selection import LeaveOneOut\n", + "from sklearn.model_selection import cross_val_score" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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auction_idexperimentdatehourdevice_makeplatform_osbrowseryesno
00008ef63-77a7-448b-bd1e-075f42c55e39exposed2020-07-108Generic Smartphone6Chrome Mobile00
1000eabc5-17ce-4137-8efe-44734d914446exposed2020-07-0710Generic Smartphone6Chrome Mobile00
20016d14a-ae18-4a02-a204-6ba53b52f2edexposed2020-07-052E58236Chrome Mobile WebView01
300187412-2932-4542-a8ef-3633901c98d9control2020-07-0315Samsung SM-A705FN6Facebook00
4001a7785-d3fe-4e11-a344-c8735acacc2ccontrol2020-07-0315Generic Smartphone6Chrome Mobile00
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" + ], + "text/plain": [ + " auction_id experiment date hour \\\n", + "0 0008ef63-77a7-448b-bd1e-075f42c55e39 exposed 2020-07-10 8 \n", + "1 000eabc5-17ce-4137-8efe-44734d914446 exposed 2020-07-07 10 \n", + "2 0016d14a-ae18-4a02-a204-6ba53b52f2ed exposed 2020-07-05 2 \n", + "3 00187412-2932-4542-a8ef-3633901c98d9 control 2020-07-03 15 \n", + "4 001a7785-d3fe-4e11-a344-c8735acacc2c control 2020-07-03 15 \n", + "\n", + " device_make platform_os browser yes no \n", + "0 Generic Smartphone 6 Chrome Mobile 0 0 \n", + "1 Generic Smartphone 6 Chrome Mobile 0 0 \n", + "2 E5823 6 Chrome Mobile WebView 0 1 \n", + "3 Samsung SM-A705FN 6 Facebook 0 0 \n", + "4 Generic Smartphone 6 Chrome Mobile 0 0 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sys.path.append('../Scripts')\n", + "from eda_Read_data import ReadData\n", + "loader = ReadData()\n", + "data= loader.read_csv('../Data/AdSmartABdata.csv')\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Preprocessing for modelling" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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auction_idexperimentdatehourdevice_makeplatform_osbrowseryesno
20016d14a-ae18-4a02-a204-6ba53b52f2edexposed2020-07-052E58236Chrome Mobile WebView01
16008aafdf-deef-4482-8fec-d98e3da054daexposed2020-07-0416Generic Smartphone6Chrome Mobile10
2000a1384a-5118-4d1b-925b-6cdada50318dexposed2020-07-068Generic Smartphone6Chrome Mobile01
2300b6fadb-10bd-49e3-a778-290da82f7a8dcontrol2020-07-084Samsung SM-A202F6Facebook10
2700ebf4a8-060f-4b99-93ac-c62724399483control2020-07-0315Generic Smartphone6Chrome Mobile01
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" + ], + "text/plain": [ + " auction_id experiment date hour \\\n", + "2 0016d14a-ae18-4a02-a204-6ba53b52f2ed exposed 2020-07-05 2 \n", + "16 008aafdf-deef-4482-8fec-d98e3da054da exposed 2020-07-04 16 \n", + "20 00a1384a-5118-4d1b-925b-6cdada50318d exposed 2020-07-06 8 \n", + "23 00b6fadb-10bd-49e3-a778-290da82f7a8d control 2020-07-08 4 \n", + "27 00ebf4a8-060f-4b99-93ac-c62724399483 control 2020-07-03 15 \n", + "\n", + " device_make platform_os browser yes no \n", + "2 E5823 6 Chrome Mobile WebView 0 1 \n", + "16 Generic Smartphone 6 Chrome Mobile 1 0 \n", + "20 Generic Smartphone 6 Chrome Mobile 0 1 \n", + "23 Samsung SM-A202F 6 Facebook 1 0 \n", + "27 Generic Smartphone 6 Chrome Mobile 0 1 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from eda_cleaning_data import Cleaning_excel_data\n", + "cleaner = Cleaning_excel_data()\n", + "clean_data= cleaner.drop_rows(data, 'yes', 'no')\n", + "clean_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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row_iddevice_makeplatform_osbrowserday_of_weekexperimentresult
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" + ], + "text/plain": [ + " row_id device_make platform_os browser day_of_week experiment \\\n", + "5221 5221 41 6 7 0 0 \n", + "6053 6053 43 6 2 4 0 \n", + "5531 5531 47 6 7 0 0 \n", + "6171 6171 13 6 1 0 0 \n", + "4719 4719 47 6 2 6 1 \n", + "\n", + " result \n", + "5221 1 \n", + "6053 1 \n", + "5531 1 \n", + "6171 0 \n", + "4719 0 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## generating a day of the week column from the date\n", + "clean_data['date'] = pd.to_datetime(df['date'], errors='coerce')\n", + "clean_data['day_of_week'] = clean_data['date'].dt.day_name()\n", + "\n", + "\n", + "#drop the no column\n", + "clean_data.drop(['no'], axis=1,inplace=True)\n", + "clean_data.rename(columns={'yes': 'result'},inplace=True)\n", + "\n", + "#Combining the control and exposed group \n", + "exposed_group = clean_data[clean_data['experiment'] == 'exposed']\n", + "control_group = clean_data[clean_data['experiment'] == 'control']\n", + "\n", + "#dropping nulls and indexing the data\n", + "clean_data['row_id'] = clean_data.index\n", + "clean_data.dropna(inplace=True)\n", + "clean_data = shuffle(clean_data)\n", + "\n", + "#encoding the data in categorical data into 0s and 1s\n", + "lb = LabelEncoder()\n", + "clean_data['experiment'] = lb.fit_transform(clean_data['experiment'])\n", + "clean_data['browser'] = lb.fit_transform(clean_data['browser'])\n", + "clean_data['device_make'] = lb.fit_transform(clean_data['device_make'])\n", + "clean_data['day_of_week'] = lb.fit_transform(clean_data['day_of_week'])\n", + "\n", + "#eliminating nwanted colmns\n", + "clean_data = clean_data[['row_id', 'device_make', 'platform_os', 'browser' ,'day_of_week', 'experiment','result']]\n", + "\n", + "\n", + "clean_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " row_id device_make platform_os browser day_of_week experiment \\\n", + "5221 5221 41 6 7 0 0 \n", + "6053 6053 43 6 2 4 0 \n", + "5531 5531 47 6 7 0 0 \n", + "6171 6171 13 6 1 0 0 \n", + "4719 4719 47 6 2 6 1 \n", + "5624 5624 13 6 1 2 1 \n", + "3854 3854 13 6 1 0 1 \n", + "2347 2347 13 6 1 0 0 \n", + "5014 5014 69 6 2 2 1 \n", + "6297 6297 64 6 7 5 0 \n", + "2598 2598 13 6 1 0 1 \n", + "4463 4463 48 6 7 2 1 \n", + "7267 7267 80 6 2 0 0 \n", + "4701 4701 13 6 2 0 1 \n", + "3056 3056 46 6 7 3 1 \n", + "6410 6410 114 5 5 2 0 \n", + "1595 1595 36 6 4 0 0 \n", + "7944 7944 112 6 4 0 0 \n", + "2631 2631 114 5 6 0 0 \n", + "1966 1966 12 6 4 0 0 \n", + "7274 7274 72 6 4 4 0 \n", + "4494 4494 48 6 4 0 0 \n", + "7856 7856 114 5 6 5 0 \n", + "1097 1097 114 5 5 0 1 \n", + "4871 4871 114 5 5 1 0 \n", + "4749 4749 114 5 5 2 0 \n", + "6905 6905 114 5 5 4 1 \n", + "2531 2531 18 6 0 3 1 \n", + "7465 7465 114 5 3 2 0 \n", + "901 901 18 6 0 0 0 \n", + "4706 4706 114 5 6 0 0 \n", + "\n", + " result \n", + "5221 1 \n", + "6053 1 \n", + "5531 1 \n", + "6171 0 \n", + "4719 0 \n", + "5624 1 \n", + "3854 0 \n", + "2347 0 \n", + "5014 1 \n", + "6297 1 \n", + "2598 1 \n", + "4463 0 \n", + "7267 0 \n", + "4701 1 \n", + "3056 0 \n", + "6410 0 \n", + "1595 1 \n", + "7944 1 \n", + "2631 0 \n", + "1966 0 \n", + "7274 0 \n", + "4494 1 \n", + "7856 0 \n", + "1097 0 \n", + "4871 0 \n", + "4749 0 \n", + "6905 0 \n", + "2531 1 \n", + "7465 1 \n", + "901 0 \n", + "4706 0 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "browser_groups= clean_data.groupby('browser')\n", + "#exposed_dropped_group= experiment_dropped_groups.get_group('exposed')\n", + "browser_groups.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1243, 7)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clean_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# set aside 20% of train and test data for evaluation\n", + "X_train, X_test, y_train, y_test = train_test_split(clean_data.loc[:, clean_data.columns != 'result'], clean_data['result'],\n", + " test_size=0.1)\n", + "\n", + "# Use the same function above for the validation set\n", + "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, \n", + " test_size=0.2) # 0.25 x 0.8 = 0.2" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(894, 6)\n", + "(125, 6)\n", + "(224, 6)\n", + "(894,)\n", + "(125,)\n", + "(224,)\n" + ] + } + ], + "source": [ + "print(X_train.shape)\n", + "print(X_test.shape)\n", + "print(X_val.shape)\n", + "print(y_train.shape)\n", + "print(y_test.shape)\n", + "print(y_val.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Logistic Regression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted: [0 0 0 0 0 0 1 0 0 0]\n", + "Actual: 6250 1\n", + "3372 0\n", + "3722 1\n", + "5579 0\n", + "3473 1\n", + "5831 0\n", + "6836 1\n", + "4901 0\n", + "2791 0\n", + "7375 1\n", + "Name: result, dtype: int64\n" + ] + } + ], + "source": [ + "# Define Logistic Regression Model\n", + "log = LogisticRegression()\n", + "# We fit our model with our train data\n", + "log.fit(X_train, y_train)\n", + "# Then predict results from X_test data\n", + "pred_log = log.predict(X_test)\n", + "\n", + "# See First 10 Predictions and They Actual Values\n", + "print(\"Predicted:\", pred_log[0:10])\n", + "print(\"Actual:\", y_test[0:10])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Decision Tree" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted: [1 1 1 1 0 0 1 0 0 0]\n", + "Actual: 6250 1\n", + "3372 0\n", + "3722 1\n", + "5579 0\n", + "3473 1\n", + "5831 0\n", + "6836 1\n", + "4901 0\n", + "2791 0\n", + "7375 1\n", + "Name: result, dtype: int64\n" + ] + } + ], + "source": [ + "# Define Decision Tree Model\n", + "dt = DecisionTreeClassifier()\n", + "# We fit our model with our train data\n", + "dt.fit(X_train, y_train)\n", + "# Then predict results from X_test data\n", + "pred_dt = dt.predict(X_test)\n", + "\n", + "# See First 10 Predictions and Their Actual Values\n", + "print(\"Predicted:\", pred_dt[0:10])\n", + "print(\"Actual:\", y_test[0:10])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### XGBoost" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Stella\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:793: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Actual: 6250 1\n", + "3372 0\n", + "3722 1\n", + "5579 0\n", + "3473 1\n", + "5831 0\n", + "6836 1\n", + "4901 0\n", + "2791 0\n", + "7375 1\n", + "Name: result, dtype: int64\n" + ] + } + ], + "source": [ + "# Define XGBoost Model\n", + "xgb = XGBClassifier(n_estimators=1000, learning_rate=0.05)\n", + "# We fit our model with our train data\n", + "xgb.fit(\n", + " X_train, y_train,\n", + " # That means if model don't improve it self in 5 rounds, it will stop learning\n", + " # So you can save your time and don't overtrain your model.\n", + " early_stopping_rounds=5,\n", + " # We provide Test data's to evaluate model performance\n", + " eval_set=[(X_test, y_test)],\n", + " verbose=False\n", + " )\n", + "# Then predict results from X_test data\n", + "pred_xgb = xgb.predict(X_test)\n", + "\n", + "# See First 10 Predictions and They Actual Values\n", + "# print(\"Predicted:\", pred_xgb[0:10])\n", + "print(\"Actual:\", y_test[0:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import sklearn.model_selection as model_selection\n", + "X_train, X_test, y_train, y_test = model_selection.train_test_split(clean_data.loc[:, clean_data.columns != 'result'], clean_data['result'], train_size=0.7,test_size=0.1, random_state=101)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(870, 6)\n", + "(125, 6)\n", + "(224, 6)\n", + "(870,)\n", + "(125,)\n", + "(224,)\n" + ] + } + ], + "source": [ + "print(X_train.shape)\n", + "print(X_test.shape)\n", + "print(X_val.shape)\n", + "print(y_train.shape)\n", + "print(y_test.shape)\n", + "print(y_val.shape)" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "81aff91296dac732a5adb56959044b4ffd2034639e84788241dd0350f5c69226" + }, + "kernelspec": { + "display_name": "Python 3.9.12 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}