{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":10038,"databundleVersionId":180088,"sourceType":"competition"}],"dockerImageVersionId":30558,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport json\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport json\nimport gc\n\nfrom scipy.stats import uniform\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nfrom sklearn.svm import SVR, LinearSVR\nfrom sklearn.decomposition import PCA\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.model_selection import train_test_split, RandomizedSearchCV\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-10-14T16:58:11.194301Z","iopub.execute_input":"2023-10-14T16:58:11.194985Z","iopub.status.idle":"2023-10-14T16:58:13.447352Z","shell.execute_reply.started":"2023-10-14T16:58:11.194954Z","shell.execute_reply":"2023-10-14T16:58:13.44619Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/ga-customer-revenue-prediction/sample_submission.csv\n/kaggle/input/ga-customer-revenue-prediction/train_v2.csv\n/kaggle/input/ga-customer-revenue-prediction/test_v2.csv\n/kaggle/input/ga-customer-revenue-prediction/sample_submission_v2.csv\n/kaggle/input/ga-customer-revenue-prediction/train.csv\n/kaggle/input/ga-customer-revenue-prediction/test.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"# The device, geonetwork, totals, trafficSource columns have multiple values within \n# their corresponding columns\n# Define the load_df function to substract these values\ndef load_df(csv_path='../input/train.csv', nrows=None):\n JSON_COLUMNS = ['device', 'geoNetwork', 'totals', 'trafficSource']\n \n df = pd.read_csv(csv_path, \n converters={column: json.loads for column in JSON_COLUMNS}, \n dtype={'fullVisitorId': 'str'}, # Important!!\n nrows=nrows)\n # Use json_normalize function to clean these columns which have the embedding structure\n for column in JSON_COLUMNS:\n column_as_df = pd.json_normalize(df[column])\n column_as_df.columns = [f\"{column}.{subcolumn}\" for subcolumn in column_as_df.columns]\n df = df.drop(column, axis=1).merge(column_as_df, right_index=True, left_index=True)\n print(f\"Loaded {os.path.basename(csv_path)}. Shape: {df.shape}\")\n return df","metadata":{"execution":{"iopub.status.busy":"2023-10-14T16:58:13.449478Z","iopub.execute_input":"2023-10-14T16:58:13.45069Z","iopub.status.idle":"2023-10-14T16:58:13.458424Z","shell.execute_reply.started":"2023-10-14T16:58:13.450639Z","shell.execute_reply":"2023-10-14T16:58:13.457575Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"df = load_df('/kaggle/input/ga-customer-revenue-prediction/train.csv')","metadata":{"execution":{"iopub.status.busy":"2023-10-14T16:58:13.461099Z","iopub.execute_input":"2023-10-14T16:58:13.462089Z","iopub.status.idle":"2023-10-14T16:59:33.006102Z","shell.execute_reply.started":"2023-10-14T16:58:13.462052Z","shell.execute_reply":"2023-10-14T16:59:33.004999Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Loaded train.csv. Shape: (903653, 55)\n","output_type":"stream"}]},{"cell_type":"code","source":"df.columns","metadata":{"execution":{"iopub.status.busy":"2023-10-14T16:59:33.008945Z","iopub.execute_input":"2023-10-14T16:59:33.009635Z","iopub.status.idle":"2023-10-14T16:59:33.017207Z","shell.execute_reply.started":"2023-10-14T16:59:33.009596Z","shell.execute_reply":"2023-10-14T16:59:33.016069Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"Index(['channelGrouping', 'date', 'fullVisitorId', 'sessionId',\n 'socialEngagementType', 'visitId', 'visitNumber', 'visitStartTime',\n 'device.browser', 'device.browserVersion', 'device.browserSize',\n 'device.operatingSystem', 'device.operatingSystemVersion',\n 'device.isMobile', 'device.mobileDeviceBranding',\n 'device.mobileDeviceModel', 'device.mobileInputSelector',\n 'device.mobileDeviceInfo', 'device.mobileDeviceMarketingName',\n 'device.flashVersion', 'device.language', 'device.screenColors',\n 'device.screenResolution', 'device.deviceCategory',\n 'geoNetwork.continent', 'geoNetwork.subContinent', 'geoNetwork.country',\n 'geoNetwork.region', 'geoNetwork.metro', 'geoNetwork.city',\n 'geoNetwork.cityId', 'geoNetwork.networkDomain', 'geoNetwork.latitude',\n 'geoNetwork.longitude', 'geoNetwork.networkLocation', 'totals.visits',\n 'totals.hits', 'totals.pageviews', 'totals.bounces', 'totals.newVisits',\n 'totals.transactionRevenue', 'trafficSource.campaign',\n 'trafficSource.source', 'trafficSource.medium', 'trafficSource.keyword',\n 'trafficSource.adwordsClickInfo.criteriaParameters',\n 'trafficSource.isTrueDirect', 'trafficSource.referralPath',\n 'trafficSource.adwordsClickInfo.page',\n 'trafficSource.adwordsClickInfo.slot',\n 'trafficSource.adwordsClickInfo.gclId',\n 'trafficSource.adwordsClickInfo.adNetworkType',\n 'trafficSource.adwordsClickInfo.isVideoAd', 'trafficSource.adContent',\n 'trafficSource.campaignCode'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2023-10-14T16:59:33.018666Z","iopub.execute_input":"2023-10-14T16:59:33.01951Z","iopub.status.idle":"2023-10-14T16:59:36.606555Z","shell.execute_reply.started":"2023-10-14T16:59:33.019462Z","shell.execute_reply":"2023-10-14T16:59:36.605557Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"\nRangeIndex: 903653 entries, 0 to 903652\nData columns (total 55 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 channelGrouping 903653 non-null object\n 1 date 903653 non-null int64 \n 2 fullVisitorId 903653 non-null object\n 3 sessionId 903653 non-null object\n 4 socialEngagementType 903653 non-null object\n 5 visitId 903653 non-null int64 \n 6 visitNumber 903653 non-null int64 \n 7 visitStartTime 903653 non-null int64 \n 8 device.browser 903653 non-null object\n 9 device.browserVersion 903653 non-null object\n 10 device.browserSize 903653 non-null object\n 11 device.operatingSystem 903653 non-null object\n 12 device.operatingSystemVersion 903653 non-null object\n 13 device.isMobile 903653 non-null bool \n 14 device.mobileDeviceBranding 903653 non-null object\n 15 device.mobileDeviceModel 903653 non-null object\n 16 device.mobileInputSelector 903653 non-null object\n 17 device.mobileDeviceInfo 903653 non-null object\n 18 device.mobileDeviceMarketingName 903653 non-null object\n 19 device.flashVersion 903653 non-null object\n 20 device.language 903653 non-null object\n 21 device.screenColors 903653 non-null object\n 22 device.screenResolution 903653 non-null object\n 23 device.deviceCategory 903653 non-null object\n 24 geoNetwork.continent 903653 non-null object\n 25 geoNetwork.subContinent 903653 non-null object\n 26 geoNetwork.country 903653 non-null object\n 27 geoNetwork.region 903653 non-null object\n 28 geoNetwork.metro 903653 non-null object\n 29 geoNetwork.city 903653 non-null object\n 30 geoNetwork.cityId 903653 non-null object\n 31 geoNetwork.networkDomain 903653 non-null object\n 32 geoNetwork.latitude 903653 non-null object\n 33 geoNetwork.longitude 903653 non-null object\n 34 geoNetwork.networkLocation 903653 non-null object\n 35 totals.visits 903653 non-null object\n 36 totals.hits 903653 non-null object\n 37 totals.pageviews 903553 non-null object\n 38 totals.bounces 450630 non-null object\n 39 totals.newVisits 703060 non-null object\n 40 totals.transactionRevenue 11515 non-null object\n 41 trafficSource.campaign 903653 non-null object\n 42 trafficSource.source 903653 non-null object\n 43 trafficSource.medium 903653 non-null object\n 44 trafficSource.keyword 400724 non-null object\n 45 trafficSource.adwordsClickInfo.criteriaParameters 903653 non-null object\n 46 trafficSource.isTrueDirect 274005 non-null object\n 47 trafficSource.referralPath 330941 non-null object\n 48 trafficSource.adwordsClickInfo.page 21460 non-null object\n 49 trafficSource.adwordsClickInfo.slot 21460 non-null object\n 50 trafficSource.adwordsClickInfo.gclId 21561 non-null object\n 51 trafficSource.adwordsClickInfo.adNetworkType 21460 non-null object\n 52 trafficSource.adwordsClickInfo.isVideoAd 21460 non-null object\n 53 trafficSource.adContent 10946 non-null object\n 54 trafficSource.campaignCode 1 non-null object\ndtypes: bool(1), int64(4), object(50)\nmemory usage: 373.2+ MB\n","output_type":"stream"}]},{"cell_type":"code","source":"# The df.info shows that for columns in [totals.bounces, totlas, newVisits, transaction Revenue, trafficSource.keyword, trafficSource.isTrueDirect, trafficSource.referralPath, 50 trafficSource.adwordsClickInfo.gclId 21561 non-null objtrafficSource.adwordsClickInfo.adNetworkType\n# trafficSource.adwordsClickInfo.isVideoAd, trafficSource.adContent, trafficSource.campaignCode]\n# There are NULL values.\n# First check columns contain nulls with their null percentage\n# Then CLean up the data\nnull_percentage = pd.DataFrame()\nfor col in df.columns:\n if df[col].isnull().sum() > 0:\n null_percentage.loc[col,'NullPercentage'] = (df[col].isnull().sum())/len(df) * 100 \nprint(null_percentage)","metadata":{"execution":{"iopub.status.busy":"2023-10-14T16:59:36.607829Z","iopub.execute_input":"2023-10-14T16:59:36.608099Z","iopub.status.idle":"2023-10-14T16:59:40.620687Z","shell.execute_reply.started":"2023-10-14T16:59:36.608075Z","shell.execute_reply":"2023-10-14T16:59:40.619891Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":" NullPercentage\ntotals.pageviews 0.011066\ntotals.bounces 50.132407\ntotals.newVisits 22.198012\ntotals.transactionRevenue 98.725728\ntrafficSource.keyword 55.655102\ntrafficSource.isTrueDirect 69.678073\ntrafficSource.referralPath 63.377425\ntrafficSource.adwordsClickInfo.page 97.625195\ntrafficSource.adwordsClickInfo.slot 97.625195\ntrafficSource.adwordsClickInfo.gclId 97.614018\ntrafficSource.adwordsClickInfo.adNetworkType 97.625195\ntrafficSource.adwordsClickInfo.isVideoAd 97.625195\ntrafficSource.adContent 98.788694\ntrafficSource.campaignCode 99.999889\n","output_type":"stream"}]},{"cell_type":"markdown","source":"Note that there is 98.9% null percentage in our target column 'totals.transactionRevenue', while it is normal, meaning that most of the customers didn't make an trascation.\n'trafficSource.adContent' and 'trafficSource.campaignCode' contains too much NULL value, thus we consider to drop them.\n'totals.newVisits' & 'totals.pageviews' could be imputed and filled by mode.","metadata":{}},{"cell_type":"code","source":"def clean_up(df):\n df.drop('trafficSource.adContent', axis=1, inplace=True)\n df.drop('trafficSource.campaignCode', axis=1, inplace=True)\n df[\"totals.transactionRevenue\"] = df['totals.transactionRevenue'].fillna(\"0\").astype(float)\n \n df[\"totals.pageviews\"] = df[\"totals.pageviews\"].fillna(0).astype(int)\n df[\"totals.newVisits\"] = df[\"totals.newVisits\"].fillna(0).astype(int)\n return df\n\ndf = clean_up(df)\ndf_rf = df","metadata":{"execution":{"iopub.status.busy":"2023-10-14T16:59:40.62172Z","iopub.execute_input":"2023-10-14T16:59:40.62216Z","iopub.status.idle":"2023-10-14T16:59:43.78163Z","shell.execute_reply.started":"2023-10-14T16:59:40.622134Z","shell.execute_reply":"2023-10-14T16:59:43.780779Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"# Convert columns to float type\ndf['totals.hits'] = df['totals.hits'].astype(float)\ndf['totals.pageviews'] = df['totals.pageviews'].astype(float)\ndf['totals.bounces'] = df['totals.bounces'].astype(float)\ndf['totals.newVisits'] = df['totals.newVisits'].astype(float)\ndf['totals.transactionRevenue'] = df['totals.transactionRevenue'].astype(float)\n\n# Convert 'date' column to datetime format\ndf['date'] = pd.to_datetime(df['date'], format='%Y%m%d')","metadata":{"execution":{"iopub.status.busy":"2023-10-14T16:59:43.783256Z","iopub.execute_input":"2023-10-14T16:59:43.784013Z","iopub.status.idle":"2023-10-14T16:59:43.990045Z","shell.execute_reply.started":"2023-10-14T16:59:43.783975Z","shell.execute_reply":"2023-10-14T16:59:43.988948Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"df.describe().to_csv('df.describe')","metadata":{"execution":{"iopub.status.busy":"2023-10-14T16:59:43.991882Z","iopub.execute_input":"2023-10-14T16:59:43.992648Z","iopub.status.idle":"2023-10-14T16:59:44.244117Z","shell.execute_reply.started":"2023-10-14T16:59:43.99261Z","shell.execute_reply":"2023-10-14T16:59:44.24333Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"df.head(5)","metadata":{"execution":{"iopub.status.busy":"2023-10-14T16:59:44.248214Z","iopub.execute_input":"2023-10-14T16:59:44.249168Z","iopub.status.idle":"2023-10-14T16:59:44.271678Z","shell.execute_reply.started":"2023-10-14T16:59:44.24911Z","shell.execute_reply":"2023-10-14T16:59:44.270399Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":" channelGrouping date fullVisitorId \\\n0 Organic Search 2016-09-02 1131660440785968503 \n1 Organic Search 2016-09-02 377306020877927890 \n2 Organic Search 2016-09-02 3895546263509774583 \n3 Organic Search 2016-09-02 4763447161404445595 \n4 Organic Search 2016-09-02 27294437909732085 \n\n sessionId socialEngagementType visitId \\\n0 1131660440785968503_1472830385 Not Socially Engaged 1472830385 \n1 377306020877927890_1472880147 Not Socially Engaged 1472880147 \n2 3895546263509774583_1472865386 Not Socially Engaged 1472865386 \n3 4763447161404445595_1472881213 Not Socially Engaged 1472881213 \n4 27294437909732085_1472822600 Not Socially Engaged 1472822600 \n\n visitNumber visitStartTime device.browser device.browserVersion \\\n0 1 1472830385 Chrome not available in demo dataset \n1 1 1472880147 Firefox not available in demo dataset \n2 1 1472865386 Chrome not available in demo dataset \n3 1 1472881213 UC Browser not available in demo dataset \n4 2 1472822600 Chrome not available in demo dataset \n\n ... trafficSource.medium trafficSource.keyword \\\n0 ... organic (not provided) \n1 ... organic (not provided) \n2 ... organic (not provided) \n3 ... organic google + online \n4 ... organic (not provided) \n\n trafficSource.adwordsClickInfo.criteriaParameters \\\n0 not available in demo dataset \n1 not available in demo dataset \n2 not available in demo dataset \n3 not available in demo dataset \n4 not available in demo dataset \n\n trafficSource.isTrueDirect trafficSource.referralPath \\\n0 NaN NaN \n1 NaN NaN \n2 NaN NaN \n3 NaN NaN \n4 True NaN \n\n trafficSource.adwordsClickInfo.page trafficSource.adwordsClickInfo.slot \\\n0 NaN NaN \n1 NaN NaN \n2 NaN NaN \n3 NaN NaN \n4 NaN NaN \n\n trafficSource.adwordsClickInfo.gclId \\\n0 NaN \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n\n trafficSource.adwordsClickInfo.adNetworkType \\\n0 NaN \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n\n trafficSource.adwordsClickInfo.isVideoAd \n0 NaN \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n\n[5 rows x 53 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
channelGroupingdatefullVisitorIdsessionIdsocialEngagementTypevisitIdvisitNumbervisitStartTimedevice.browserdevice.browserVersion...trafficSource.mediumtrafficSource.keywordtrafficSource.adwordsClickInfo.criteriaParameterstrafficSource.isTrueDirecttrafficSource.referralPathtrafficSource.adwordsClickInfo.pagetrafficSource.adwordsClickInfo.slottrafficSource.adwordsClickInfo.gclIdtrafficSource.adwordsClickInfo.adNetworkTypetrafficSource.adwordsClickInfo.isVideoAd
0Organic Search2016-09-0211316604407859685031131660440785968503_1472830385Not Socially Engaged147283038511472830385Chromenot available in demo dataset...organic(not provided)not available in demo datasetNaNNaNNaNNaNNaNNaNNaN
1Organic Search2016-09-02377306020877927890377306020877927890_1472880147Not Socially Engaged147288014711472880147Firefoxnot available in demo dataset...organic(not provided)not available in demo datasetNaNNaNNaNNaNNaNNaNNaN
2Organic Search2016-09-0238955462635097745833895546263509774583_1472865386Not Socially Engaged147286538611472865386Chromenot available in demo dataset...organic(not provided)not available in demo datasetNaNNaNNaNNaNNaNNaNNaN
3Organic Search2016-09-0247634471614044455954763447161404445595_1472881213Not Socially Engaged147288121311472881213UC Browsernot available in demo dataset...organicgoogle + onlinenot available in demo datasetNaNNaNNaNNaNNaNNaNNaN
4Organic Search2016-09-022729443790973208527294437909732085_1472822600Not Socially Engaged147282260021472822600Chromenot available in demo dataset...organic(not provided)not available in demo datasetTrueNaNNaNNaNNaNNaNNaN
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5 rows × 53 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"2. Exploratory Data Analysis (EDA):\n\nThis phase involves visualizing the data to find patterns, relationships, and anomalies.\n\nDistribution of the target variable (revenue per customer).\nCorrelation analysis to see how different variables relate to the target.\nVisualization of important variables, possibly using histograms, scatter plots, boxplots.\nAnalysis of revenue distribution across different customer segment","metadata":{}},{"cell_type":"code","source":"# Select the top 10 countries with most transcation records\ntop_countries = df[[\"geoNetwork.country\"]].groupby(\"geoNetwork.country\").size().sort_values(ascending=False)[:10].index.to_numpy()\navg_totals_transactionRevenue = df.groupby('geoNetwork.country')['totals.transactionRevenue'].mean().reset_index()\ndf_top_countries = avg_totals_transactionRevenue[avg_totals_transactionRevenue[\"geoNetwork.country\"].isin(top_countries)]\ndf_top_countries\n\nplt.figure(figsize=(18,4))\nsns.barplot(data=df_top_countries, x=\"geoNetwork.country\", y=\"totals.transactionRevenue\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-14T16:59:44.272951Z","iopub.execute_input":"2023-10-14T16:59:44.273235Z","iopub.status.idle":"2023-10-14T16:59:45.069223Z","shell.execute_reply.started":"2023-10-14T16:59:44.273211Z","shell.execute_reply":"2023-10-14T16:59:45.068169Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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the impact of geoNetwork.country, geoNetwork.region, geoNetwork.metro and geoNetwork.city","metadata":{}},{"cell_type":"code","source":"# Select the top 10 cities with most transcation records\ntop_cities = df[[\"geoNetwork.city\"]].groupby(\"geoNetwork.city\").size().sort_values(ascending=False)[:10].index.to_numpy()\navg_totals_transactionRevenue = df.groupby('geoNetwork.city')['totals.transactionRevenue'].mean().reset_index()\ndf_top_cities = avg_totals_transactionRevenue[avg_totals_transactionRevenue[\"geoNetwork.city\"].isin(top_cities)]\ndf_top_cities\n\nplt.figure(figsize=(18,4))\nsns.barplot(data=df_top_cities, x=\"geoNetwork.city\", y=\"totals.transactionRevenue\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-14T16:59:45.070628Z","iopub.execute_input":"2023-10-14T16:59:45.070899Z","iopub.status.idle":"2023-10-14T16:59:45.866039Z","shell.execute_reply.started":"2023-10-14T16:59:45.070875Z","shell.execute_reply":"2023-10-14T16:59:45.864925Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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Select the top 10 regions with most transcation records\ntop_regions = df[[\"geoNetwork.region\"]].groupby(\"geoNetwork.region\").size().sort_values(ascending=False)[:10].index.to_numpy()\navg_totals_transactionRevenue = df.groupby('geoNetwork.region')['totals.transactionRevenue'].mean().reset_index()\ndf_top_regions = avg_totals_transactionRevenue[avg_totals_transactionRevenue[\"geoNetwork.region\"].isin(top_regions)]\ndf_top_regions\n\nplt.figure(figsize=(18,4))\nsns.barplot(data=df_top_regions, x=\"geoNetwork.region\", y=\"totals.transactionRevenue\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-14T16:59:45.867421Z","iopub.execute_input":"2023-10-14T16:59:45.867775Z","iopub.status.idle":"2023-10-14T16:59:46.636096Z","shell.execute_reply.started":"2023-10-14T16:59:45.867746Z","shell.execute_reply":"2023-10-14T16:59:46.635082Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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and bivariate analysis","metadata":{}},{"cell_type":"markdown","source":"Univariate: categorical variable","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Assuming df is the loaded dataframe\nsns.set_style('whitegrid')\n\n\n# List of categorical columns for univariate analysis\ncategorical_cols = ['channelGrouping', 'device.browser', 'device.operatingSystem', 'device.isMobile', \n 'device.deviceCategory', 'geoNetwork.continent', 'geoNetwork.subContinent', \n 'trafficSource.medium']\n\nfor col in categorical_cols:\n plt.figure(figsize=(12, 6))\n sns.countplot(y=col, data=df, order=df[col].value_counts().index)\n plt.title(f'Distribution of {col}')\n plt.xlabel('Count')\n plt.ylabel(col)\n plt.show()\n","metadata":{"execution":{"iopub.status.busy":"2023-10-14T16:59:46.637525Z","iopub.execute_input":"2023-10-14T16:59:46.638272Z","iopub.status.idle":"2023-10-14T16:59:56.380179Z","shell.execute_reply.started":"2023-10-14T16:59:46.638231Z","shell.execute_reply":"2023-10-14T16:59:56.378993Z"},"trusted":true},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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xBOAACyjW+//VaSVK9evVv28/LyUkREhCIiIjR06FB9/PHHGj9+vDZu3KjIyMibfmm5U4cOHXJ6bRiGDh065PQF62ZXKBw7dszpUn9XavP391dsbKwuXLjgdPVEfHy8Y7k7+Pv7a/fu3bLb7U5XT6TtJ21iyKx27tw5xcbGql+/fk63Wxw8eNDt+ypbtqzWrl2rs2fP3vbqiRt5e3vLZrPp4MGDOnPmjHx9fZU3b14dOHAgXd/4+Hh5eXk5rmpJuy0pKSnJ8bPk2tUpZcuW1d69e2/bZ9euXYqIiHD7+XA7vr6+yp8/v+x2uyIjI2/Z19/fX3v27JFhGE51ZnQsb5R2/G68oiarr2gCAGQN5pwAAGQLsbGx+uijj1S6dGk9/vjjN+139uzZdG1Vq1aVJMfjB/PmzStJbrsse+HChU5zBixdulQnT55UgwYNHG1lypTR77//7vQIxP/973/pHjnqSm0NGjRQamqq43GHaWbMmCGLxeK0/3+jQYMGOnnypBYvXuxoS0lJUUxMjPLly6datWrd0Xbz5cvn0hjc7F+wZ86ceUf7v5VmzZrJMAxNnDgx3bK0fzE/ePBghqFBUlKSfvvtNxUuXFi+vr6yWq2qW7euVqxY4fQo0FOnTun7779XjRo1HOFS2lVB18/jkZycrIULF7pU+65du7R8+fKb1v7II4/or7/+0tdff52uz+XLl5WcnOx4fezYMe3fvz/T+78dq9Wq5s2ba9myZdqzZ0+65dc/7rRBgwY6ceKE4xGlknTp0qUM676Rv7+/rFZrujlRvvzyy39RPQDALFw5AQC461avXq34+Hilpqbq1KlT2rhxo9atW6dSpUpp8uTJGV7un2bSpEnavHmzGjZsKH9/fyUmJmr27NkqWbKk49GcZcuWVaFChfTVV18pf/78ypcvn0JDQ+/4sYCFCxdWp06d1KZNG8ejRMuVK+c0aV+7du20bNky9ejRQ4888ogSEhK0aNGidLeouFJb48aNVbt2bY0fP15Hjx6VzWbTunXrtGLFCj377LO3vP3FFR06dNCcOXMUHR2tHTt2yN/fX8uWLdOvv/6qYcOGpZvzIrOCgoIUGxur6dOnq0SJEipduvQtH6lZoEAB1apVS1OnTtW1a9d03333ad26dU5f+N2lTp06euKJJxQTE6NDhw6pfv36stvt2rJli2rXrq0uXbpo165dGjx4sOrXr6+aNWuqcOHC+uuvv7Rw4UKdOHFCw4YNcwQqUVFRWr9+vTp16qROnTrJarVqzpw5unr1ql5++WXHfuvWratSpUrp1VdfVXx8vKxWq+bNm6eiRYtm+uqJ559/XsuWLdNLL72kp556SkFBQTp37px+/vlnvf3226pSpYqeeOIJLVmyRG+++aY2btyo8PBwpaamKj4+XkuXLtXUqVMVEhIiSRoyZIg2bdqk3bt3u+34Dho0SBs3blT79u3Vrl07Va5cWefOndOOHTsUGxurTZs2SZLat2+vWbNmaciQIdqxY4f8/Pwy/YjeggULqkWLFvriiy9ksVhUpkwZrVy5MlPzaQAAsh/CCQDAXTdhwgRJf18eX6RIEQUEBGjYsGFq06bNbb8IN27cWEePHtW8efN05swZFS1aVA8++KD69evnmJjP29tbo0aN0rhx4/TWW28pJSVFI0eOvONwonfv3tq9e7c++eQTXbx4UREREXrzzTcdV0FIUv369RUdHa3p06drxIgRCg4O1scff6z333/faVuu1Obl5aXJkydrwoQJWrx4sebPny9/f3+98sor6t69+x29l4zkyZNHMTExGjNmjBYsWKALFy6oQoUKGjlypEtPNblRdHS03njjDf33v//V5cuX1bp161uGE5I0duxYvfvuu5o9e7YMw1DdunX16aefpntihTuMHDlSNptNc+fO1ejRo1WwYEEFBwerevXqkqRatWqpf//+WrNmjaZPn64zZ84of/78qlq1qgYPHqzmzZs7tvXAAw9o1qxZGjt2rKZMmSLDMBQaGqoPPvjA6T17e3tr4sSJevvtt/V///d/8vPz07PPPqtChQrddH6IG+XPn1+zZs3Shx9+qOXLl2vBggUqVqyYIiIidN9990n6+7MzadIkzZgxQ99++62WL1+uvHnzqnTp0uratWuWTDh5veLFi+ubb77RpEmTtHz5cn355ZcqUqSIKleurMGDBzv65c2bVzNmzNC7776rL774Qnny5NFjjz2mBg0aqEePHrfdz2uvvaaUlBR99dVX8vHxUYsWLfTKK684PXkGAHBvsBjM9gMAAAAAAEzEnBMAAAAAAMBUhBMAAAAAAMBUhBMAAAAAAMBUhBMAAAAAAMBUhBMAAAAAAMBUhBMAAAAAAMBUucwuAJLdbldKSoq8vLxksVjMLgcAAAAA4OEMw5DdbleuXLnk5WX+dQuEE9lASkqKtm/fbnYZAAAAAIAcJiQkRD4+PmaXQTiRHaSlVIGBgdniQ4F/JzU1Vdu3b1dISIisVqvZ5cANGFPPwnh6FsbTszCenoXx9CyMp2dJG09J2eKqCYlwIltIu5XDarVyonsQxtPzMKaehfH0LIynZ2E8PQvj6VkYT8+TXaYWyB4RCQAAAAAAyLEIJwAAAAAAgKkIJ4AskDdvXrNLgJsxpp6F8fQsjKdnYTw9C+PpWRhPZCWLYRiG2UXkdKmpqdq6davCwsK4fwsAAAAA7lGpdrus2WSCyVtJ+w4qKdt8D2VCzGzk3W/Wa++f58wuAwAAAADgogolCmt4p/pml3HPIpzIRg6dTNKuo6fNLgMAAAAAgLsq+19vAgAAAAAAPBrhBAAAAAAAMBXhBAAAAAAAMBXhBAAAAAAAMBXhBAAAAAAAMBXhBAAAAAAAMBXhBAAAAAAAMBXhBAAAAAAAMBXhBAAAAAAAMBXhBAAAAAAAMBXhBAAAAAAAMBXhBAAAAAAAMBXhBAAAAAAAMBXhBAAAAAAAMBXhBAAAAAAAMBXhBAAAAAAAMBXhBAAAAAAAMBXhBAAAAAAAMBXhBAAAAAAAMBXhxB368MMP9cQTT5hdBgAAAAAA97xcZhdwr+revbu6dOlidhkAAAAAANzzPCacMAxDqampypXr7ryl/PnzK3/+/HdlXwAAAAAAeLJsfVvH1atXNXz4cEVERCgkJERPP/20tm3bJknauHGjbDabVq1apTZt2igkJERbtmzRhQsXNGjQIIWFhalevXqaMWOGunbtqvfee8+x3YULF6pNmzaqXr266tatq0GDBikxMdGxPG3bsbGxatOmjapVq6aOHTsqPj7e0Sej2zrmzp2rli1bKjg4WPXq1dM777yTxUcIAAAAAIB7X7YOJ0aPHq1ly5Zp1KhRWrBggcqVK6cePXro7Nmzjj5jx47VoEGDtHjxYtlsNo0aNUq//fabJk+erM8++0ybN2/Wjh07nLabkpKil156Sd99950mTZqko0ePKjo6Ot3+x48fr+joaM2bN09Wq1XDhg27aa2zZ8/WO++8o/bt22vRokX66KOPVLZsWbcdCwAAAAAAPFW2va0jOTlZX331lUaOHKmGDRtKkt59912tW7dOc+fOVUhIiCSpf//+qlu3riTpwoULWrhwocaMGaOIiAhJ0siRI1W/fn2nbbdt29bxc5kyZfTqq6+qbdu2unjxotOtGgMGDNCDDz4oSerVq5d69eqlK1euKHfu3OnqnTx5sp577jk9++yzjrbQ0FB3HAoAAAAAADxatg0nEhISdO3aNYWHhzvavL29FRoaqv379zvCibT/StKRI0d07do1p1CgYMGCqlChgtO2//jjD02cOFG7du3SuXPnZBiGJOn48eOqXLmyo5/NZnP87OfnJ0lKTExUqVKlnLaXmJioEydOOAIRAAAAAACQedk2nMisvHnzutQ/OTlZzz//vOrVq6cxY8aoaNGiOn78uJ5//nldu3bNqe/1k2taLBZJkt1uT7fNjK6kAAAAAAAAmZNt55woW7asvL299euvvzrarl27pu3btztd3XC90qVLy9vbW9u3b3e0nT9/XgcPHnS8jo+P19mzZzV48GDVrFlTlSpVcpoM804UKFBA/v7+io2N/VfbAQAAAAAgJ8q2V07ky5dPTz/9tEaPHq3ChQurVKlSmjp1qi5fvqy2bdtq165d6dYpUKCAnnzyScc6xYoV04cffiiLxeK48qFUqVLy9vZWTEyMnn76ae3Zs0cfffTRv663X79+evPNN1WsWDE1aNBAFy9e1K+//qquXbv+620DAAAAAODJsm04IUmDBw+WYRh65ZVXdPHiRQUHB2vq1KkqXLjwTdeJjo7Wm2++qd69e6tAgQLq0aOHjh8/7rj1wtfXV6NGjdK4ceMUExOjoKAgDRkyRH369PlXtbZu3VpXrlzRjBkzNHr0aBUpUkQtWrT4V9sEAAAAACAnsBhps0F6qOTkZDVo0EBDhgxRu3btzC4nQ6mpqdq6dasmrv9T2xL+3S0mAAAAAIC7r4q/r2ZFtTK7jExJ+w4qSWFhYbJareYWpGx+5cSd2Llzp+Lj4xUaGqrz589r0qRJkqQmTZqYXBkAAAAAAMiIx4UTkvTZZ5/pwIED8vb2VlBQkGbNmiVfX1+zywIAAAAAABnwuHAiMDBQ8+fPN7sMAAAAAACQSdn2UaIAAAAAACBnIJwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmymV2AfhHOb9CuppqmF0GAAAAAMBFFUoUNruEexrhRDbyertIWa1Ws8sAAAAAANyBVLtdVi9uULgTHLVsJDU11ewS4AapqanauXMn4+lBGFPPwnh6FsbTszCenoXx9CyMZ+YQTNw5jhyQBS5dumR2CXAzxtSzMJ6ehfH0LIynZ2E8PQvjiaxEO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z8FBwfL29vbaZzi4+N17NgxhYWFSZLCwsK0Z88epxN3/fr1KlCggONSurCwMG3YsMFpP+vXr3dsg89D1ihdurT8/PycjuuFCxf0+++/O45r9erVlZSUpD/++MPRZ8OGDbLb7QoNDZX09/ht3rxZ165dc/RZv369KlSo4Ljf+nZjnJla4Lo///xTZ8+edfwFyHhmH4Zh6J133tHy5cs1c+ZMlSlTxml5dvr9mplacrrbjWdG4uLiJP3zP8KMZ/Znt9t19epVzk8PkTaeGeH8zL7q1KmjRYsWaeHChY4/wcHBeuyxxxw/e+T56dL0mXC7H374wQgODjbmz59v7Nu3z3j99deNmjVrOs2qiqw3atQoY+PGjcbhw4eNLVu2GN26dTNq167teJzoG2+8YTz00ENGbGyssX37dqNDhw4ZPqqne/fuRlxcnLF69WqjTp06GT6q5/333zf27dtnfPHFFxk+qofPg+suXLhg7Ny509i5c6cREBBgTJ8+3di5c6dx9OhRwzD+ftxjzZo1jZ9++snYtWuX0adPnwwfJfrkk08av//+u7F582ajWbNmTo+eTEpKMiIjI42XX37Z2LNnj/HDDz8Y1apVS/foycDAQGPatGnGvn37jAkTJmT46Mnb1ZLT3Wo8L1y4YIwaNcr47bffjMOHDxvr1683WrdubTRr1szpyRuMZ/bw5ptvGjVq1DA2btzo9Oi6S5cuOfpkp9+vt6slp7vdeB46dMiYOHGisX37duPw4cPGTz/9ZDRp0sTo3LmzYxuMZ/YyZswYY9OmTcbhw4eNXbt2GWPGjDFsNpuxdu1awzA4P+81txpPzs97341PW/HE85NwIhuIiYkxHnroISMoKMho27atsXXrVrNLynGioqKMunXrGkFBQUb9+vWNqKgo49ChQ47lly9fNt566y2jVq1aRrVq1YwXX3zROHHihNM2jhw5YvTo0cMIDQ01ateubYwaNcq4du2aU58NGzYYTzzxhBEUFGQ0adLEmDdvXrpa+Dy4bsOGDUZAQEC6P0OGDDEM4+9HPv73v/81IiMjjeDgYOPZZ5814uPjnbZx5swZY+DAgUZYWJgRHh5uREdHGxcuXHDqExcXZzz99NNGcHCwUb9+/Qwf8bp48WKjWbNmRlBQkNGyZUtj5cqVTsszU0tOd6vxvHTpktG9e3ejTp06RlBQkNGoUSPjtddeSxfgMZ7ZQ0bjGBAQ4PS7Lzv9fs1MLTnZ7cbz2LFjRufOnY0HH3zQCA4ONpo2bWq8//77xvnz5522w3hmH0OHDjUaNWpkBAUFGXXq1DGeffZZRzBhGJyf95pbjSfn573vxnDCE89Pi2EYhmvXWgAAAAAAALgPc04AAAAAAABTEU4AAAAAAABTEU4AAAAAAABTEU4AAAAAAABTEU4AAAAAAABTEU4AAAAAAABTEU4AAAAAAABTEU4AAAAAAABTEU4AAAAAAABTEU4AAADTnDx5Uu+++66aNGmi4OBgNWzYUL1791ZsbOxdrcNms+mnn366q/sEAAD/yGV2AQAAIGc6cuSInn76aRUqVEivvPKKAgIClJKSorVr1+rtt9/W0qVLzS4RAADcJRbDMAyziwAAADlPz549tXv3bi1dulT58uVzWpaUlKRChQrp2LFjevfdd7VhwwZZLBbVr19fr7/+uooXLy5Jio6OVlJSkj766CPHuu+995527dqlmJgYSVLXrl1ls9nk4+OjuXPnytvbWx07dlS/fv0kSY0bN9bRo0cd6/v7++vnn3/O6rcPAACuw20dAADgrjt79qzWrFmjzp07pwsmJKlQoUKy2+3q27evzp07p5iYGE2fPl2HDx/WgAEDXN7fggULlC9fPn399dd6+eWXNWnSJK1bt06SNHfuXEnSyJEjtXbtWsdrAABw93BbBwAAuOsSEhJkGIYqVqx40z6xsbHas2ePVqxYofvvv1+SNHr0aLVs2VLbtm1TaGhopvdns9n0n//8R5JUvnx5ffHFF4qNjVXdunXl6+sr6e9AxM/P71+8KwAAcKe4cgIAANx1mbmrdP/+/SpZsqQjmJCkypUrq1ChQoqPj3dpfzabzem1n5+fEhMTXdoGAADIOoQTAADgritXrpwsFovLIcONLBZLuqAjJSUlXb9cuZwvFs1oPQAAYB7CCQAAcNcVKVJE9erV06xZs5ScnJxueVJSkipVqqQ///xTx48fd7Tv27fPsUySfH19dfLkSad14+LiXK7H29tbqampLq8HAADcg3ACAACY4s0335Tdble7du20bNkyHTx4UPv379fnn3+uDh06KDIyUgEBARo8eLB27Nihbdu26ZVXXtGDDz6okJAQSVKdOnX0xx9/aOHChTp48KAmTJigvXv3ulyLv7+/YmNjdfLkSZ07d87dbxUAANwG4QQAADBFmTJlNH/+fNWuXVvvv/++WrVqpeeee06xsbF66623ZLFY9NFHH6lQoULq0qWLunXrpjJlymj8+PGObdSvX199+/bVBx98oLZt2+rixYt68sknXa5lyJAhWr9+vR566CG1bt3aje8SAABkhsXghksAAAAAAGAirpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACmIpwAAAAAAACm+n8wNYmUwsRq4QAAAABJRU5ErkJggg=="},"metadata":{}}]},{"cell_type":"code","source":"# Bivariate Analysis for channelGrouping vs. totals.transactionRevenue\nplt.figure(figsize=(10,6))\nsns.boxplot(x='channelGrouping', y='totals.transactionRevenue', data=df)\nplt.title('Channel Grouping vs Transaction Revenue')\nplt.xticks(rotation=45)\nplt.show()\n\n# Bivariate Analysis for totals.hits vs. totals.transactionRevenue\nplt.figure(figsize=(10,6))\nsns.scatterplot(x='totals.hits', y='totals.transactionRevenue', data=df)\nplt.title('Hits vs Transaction Revenue')\nplt.show()\n\n# Bivariate Analysis for device.browser vs. totals.transactionRevenue\nplt.figure(figsize=(12, 8))\nsns.boxplot(x='device.browser', y='totals.transactionRevenue', data=df)\nplt.title('Device Browser vs Transaction Revenue')\nplt.xticks(rotation=90)\nplt.show()\n\n# Bivariate Analysis for device.isMobile vs. totals.transactionRevenue\nplt.figure(figsize=(10,6))\nsns.boxplot(x='device.isMobile', y='totals.transactionRevenue', data=df)\nplt.title('Is Mobile vs Transaction Revenue')\nplt.show()\n\n# Bivariate Analysis for geoNetwork.continent vs. totals.transactionRevenue\nplt.figure(figsize=(10,6))\nsns.boxplot(x='geoNetwork.continent', y='totals.transactionRevenue', data=df)\nplt.title('Continent vs Transaction Revenue')\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2023-10-14T16:59:56.381583Z","iopub.execute_input":"2023-10-14T16:59:56.38188Z","iopub.status.idle":"2023-10-14T17:00:03.207691Z","shell.execute_reply.started":"2023-10-14T16:59:56.381855Z","shell.execute_reply":"2023-10-14T17:00:03.20661Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"3. Correlation\ncorrelation plot with total_revenue","metadata":{}},{"cell_type":"code","source":"# List of numerical columns for correlation\nnumerical_cols = ['totals.hits', 'totals.pageviews']\n\n# Calculate correlation with 'totals.transactionRevenue'\ncorrelations = df[numerical_cols + ['totals.transactionRevenue']].corr()['totals.transactionRevenue'].drop('totals.transactionRevenue')\n\n# Plot\nplt.figure(figsize=(8,6))\ncorrelations.plot(kind='bar')\nplt.title('Correlation with Transaction Revenue')\nplt.ylabel('Correlation coefficient')\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2023-10-14T17:00:03.210232Z","iopub.execute_input":"2023-10-14T17:00:03.210591Z","iopub.status.idle":"2023-10-14T17:00:03.553655Z","shell.execute_reply.started":"2023-10-14T17:00:03.210541Z","shell.execute_reply":"2023-10-14T17:00:03.552593Z"},"trusted":true},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"# List of numerical columns for correlation\nnumerical_cols = ['totals.hits', 'totals.pageviews', 'totals.bounces', 'totals.newVisits']\n\n# Compute correlations\ncorrelation_matrix = df[numerical_cols + ['totals.transactionRevenue']].corr()\n\n# Plot using heatmap\nplt.figure(figsize=(10, 8))\nsns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0, linewidths=0.5, linecolor='black')\nplt.title('Correlation with Transaction Revenue')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-14T17:00:03.55481Z","iopub.execute_input":"2023-10-14T17:00:03.55508Z","iopub.status.idle":"2023-10-14T17:00:04.059302Z","shell.execute_reply.started":"2023-10-14T17:00:03.555054Z","shell.execute_reply":"2023-10-14T17:00:04.058245Z"},"trusted":true},"execution_count":17,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"4. Time Series Analysis\nAggregate totals.transactionRevenue by date and plot:","metadata":{}},{"cell_type":"code","source":"# Aggregate revenue by date\ntime_series_data = df.groupby('date')['totals.transactionRevenue'].sum()\n\n# Plot\nplt.figure(figsize=(14,7))\ntime_series_data.plot()\nplt.title('Transaction Revenue Over Time')\nplt.ylabel('Total Transaction Revenue')\nplt.xlabel('Date')\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2023-10-14T17:00:04.060784Z","iopub.execute_input":"2023-10-14T17:00:04.061192Z","iopub.status.idle":"2023-10-14T17:00:04.735093Z","shell.execute_reply.started":"2023-10-14T17:00:04.061152Z","shell.execute_reply":"2023-10-14T17:00:04.733928Z"},"trusted":true},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"from statsmodels.tsa.seasonal import seasonal_decompose\n\ntime_series_data = df.groupby('date')['totals.transactionRevenue'].sum()\n\n# Decompose the time series\ndecomposition = seasonal_decompose(time_series_data, model='additive', period=28) # Using annual frequency\n\n# Plot the original data, the trend, the seasonality, and the residuals \nplt.figure(figsize=(12, 8))\n\nplt.subplot(4, 1, 1)\nplt.plot(time_series_data, label='Original')\nplt.legend(loc='upper left')\nplt.title('Transaction Revenue Over Time')\n\nplt.subplot(4, 1, 2)\nplt.plot(decomposition.trend, label='Trend')\nplt.legend(loc='upper left')\nplt.title('Trend Component')\n\nplt.subplot(4, 1, 3)\nplt.plot(decomposition.seasonal, label='Seasonal')\nplt.legend(loc='upper left')\nplt.title('Seasonal Component')\n\nplt.subplot(4, 1, 4)\nplt.plot(decomposition.resid, label='Residual')\nplt.legend(loc='upper left')\nplt.title('Residual Component')\n\nplt.tight_layout()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2023-10-14T17:00:04.736397Z","iopub.execute_input":"2023-10-14T17:00:04.736706Z","iopub.status.idle":"2023-10-14T17:00:06.541984Z","shell.execute_reply.started":"2023-10-14T17:00:04.73668Z","shell.execute_reply":"2023-10-14T17:00:06.541034Z"},"trusted":true},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"**Summary Report:**\n\n**Traffic Sources & User Behavior:**\n- **Channel Distribution:** The predominant channels bringing traffic to the site are \"Social\" and \"Direct,\" followed by \"Referral.\"\n- **Device Usage:** Users primarily access the website through mobile devices, with a greater proportion using non-mobile devices. Interestingly, while mobile users contribute more to the traffic, their transaction revenues are relatively lower compared to non-mobile users.\n- **Geographic Insights:** Most users hail from the Americas, followed by Asia and Europe. This suggests a strong user base in the Western hemisphere.\n\n**Website Interactions:**\n- High correlation exists between pageviews and hits, both of which also moderately correlate with transaction revenue. This implies that user engagement on the site, indicated by hits and pageviews, could influence revenue generation.\n\n**Potential Predictive Features:**\n1. **Device Usage:** Whether a user accesses the site from a mobile or non-mobile device could be influential in predicting transaction revenues.\n2. **Geographic Location:** Continent-based segmentation can potentially influence the prediction, given the significant distribution differences.\n3. **User Engagement Metrics:** Variables like hits and pageviews, which show a correlation with transaction revenue, can be essential features for prediction.\n4. **Traffic Sources:** The channel of acquisition, particularly \"Social\" and \"Direct,\" can serve as potential predictors for user behavior and revenue outcomes.\n\n---\n\nWe are suggesting that the device type, geographical location, user engagement metrics, and traffic sources are pivotal variables. When predicting outcomes like transaction revenue, considering these can enhance the accuracy and robustness of predictive models.","metadata":{}},{"cell_type":"markdown","source":"## Feature engineering:\nWe would like to divide our model into two separate parts, one is classify whether the customer would like to purchase or not, the other is to predict how much he/she will spend.\nBased on the EDA analysis above, we will create some predictive variables that help with classification and prediction. We will also normalized several variables to make them standardized to improve our prediction accuracy. Last, we apply different weight for variables that significant contribute to the value of total.transactionRevenue\n---\n\n### 1. Classification: Predicting Purchase Intent\n\n**Objective**: Determine if a customer intends to make a purchase.\n\n**Features to Consider**:\n\n1. **Binary Device Type**: Given the prominence of mobile users, create a binary feature: `is_mobile` (1 for mobile, 0 otherwise).\n\n2. **Geographical Segmentation**: Convert continents into categorical one-hot encoded features, e.g., `from_Americas`, `from_Asia`, etc.\n\n3. **Traffic Source Encoding**: Given the importance of the 'Social' and 'Direct' channels, create binary features such as `is_from_social` and `is_from_direct`.\n\n4. **Engagement Metrics**: Use metrics like `hits` and `pageviews` as they are, given their potential correlation with intent.\n\n5. **User Behavior Patterns**: Feature indicating the user's previous purchase history or interaction frequency could be beneficial.\n\n### 2. Regression: Predicting Purchase Amount\n\n**Objective**: Estimate the transaction amount for customers who intend to purchase.\n\n**Features to Consider**:\n\n1. **Normalized Engagement Metrics**: Create features like `normalized_hits` and `normalized_pageviews` where you scale these metrics between 0 and 1.\n\n2. **Device Type Weight**: Assign weights to device types based on their average transaction values. For instance, if non-mobile users tend to spend more, assign higher weights to them.\n\n3. **Geographical Weight**: Assign weights to continents or sub-continents based on average transaction values. If users from the Americas tend to spend more, they should have a higher weight.\n\n4. **Traffic Source Weight**: Similar to the above, assign weights to traffic sources based on their contribution to revenue.\n\n5. **Previous Purchase Behavior**: If possible, incorporate features that track the average spend of a user in the past or the frequency of high-value purchases.\n\n6. **Product Interaction**: Features indicating which products/categories a user interacted with can provide insights into potential spend.\n\n### Shared Pre-processing:\n\n1. **Handle Missing Values**: Impute or remove missing values to ensure models receive complete data.\n \n2. **Scaling**: Especially for regression, ensure numerical features are scaled (e.g., using MinMaxScaler or StandardScaler) to ensure they are on similar scales.\n\n3. **Categorical Encoding**: Convert categorical variables into numerical representations using methods like one-hot encoding or target encoding.\n\n4. **Feature Selection**: Based on model importance or correlation metrics, prune features that don't contribute significantly to the model's predictive power.\n\n---","metadata":{}},{"cell_type":"code","source":"from sklearn.preprocessing import MinMaxScaler, StandardScaler, OneHotEncoder\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_report\nfrom sklearn.ensemble import RandomForestClassifier\n\n# Classification: Predicting Purchase Intent\n\n# 1. Binary Device Type\ndf['is_mobile'] = df['device.isMobile'].astype(int)\n\n# 2. Geographical Segmentation\ncontinent_dummies = pd.get_dummies(df['geoNetwork.continent'], prefix='from')\ndf = pd.concat([df, continent_dummies], axis=1)\n\n# 3. Traffic Source Encoding\ndf['is_from_social'] = (df['channelGrouping'] == 'Social').astype(int)\ndf['is_from_direct'] = (df['channelGrouping'] == 'Direct').astype(int)\n\n# Regression: Predicting Purchase Amount\n\n# 1. Normalized Engagement Metrics\nscaler = MinMaxScaler()\ndf['normalized_hits'] = scaler.fit_transform(df[['totals.hits']])\ndf['normalized_pageviews'] = scaler.fit_transform(df[['totals.pageviews']])\n\n# 2. Device Type Weight\n# Assuming avg transaction values for mobile and non-mobile are 100 and 150 respectively\ndf['device_weight'] = df['is_mobile'].apply(lambda x: 100 if x == 1 else 150)\n\n# 3. Geographical Weight\n# Dummy weights: Adjust based on your data insights\ncontinent_weights = {\n 'Americas': 150,\n 'Asia': 100,\n 'Europe': 130,\n 'Africa': 90,\n 'Oceania': 110\n}\ndf['continent_weight'] = df['geoNetwork.continent'].map(continent_weights)\n\n# 4. Traffic Source Weight\n# Dummy weights: Adjust based on your data insights\nchannel_weights = {\n 'Social': 90,\n 'Direct': 150,\n 'Referral': 120,\n 'Paid Search': 100,\n 'Affiliates': 110,\n 'Display': 95,\n '(Other)': 100\n}\ndf['channel_weight'] = df['channelGrouping'].map(channel_weights)\n\n# Shared Pre-processing\n\n# Handle Missing Values: Simple Imputation for demonstration\ndf.fillna(method='ffill', inplace=True)\n\n# Scaling\nstd_scaler = StandardScaler()\ndf[['normalized_hits', 'normalized_pageviews']] = std_scaler.fit_transform(df[['normalized_hits', 'normalized_pageviews']])\n\n# Categorical Encoding: Demonstrating with continent column\nencoder = OneHotEncoder(drop='first')\ncontinent_encoded = encoder.fit_transform(df[['geoNetwork.continent']])\n\n# Use get_feature_names_out to get the transformed column names\nencoded_feature_names = encoder.get_feature_names_out(['geoNetwork.continent'])\n\ncontinent_df = pd.DataFrame(continent_encoded.toarray(), columns=encoded_feature_names)\ndf = pd.concat([df, continent_df], axis=1)","metadata":{"execution":{"iopub.status.busy":"2023-10-14T17:00:06.543328Z","iopub.execute_input":"2023-10-14T17:00:06.543667Z","iopub.status.idle":"2023-10-14T17:00:15.440532Z","shell.execute_reply.started":"2023-10-14T17:00:06.543639Z","shell.execute_reply":"2023-10-14T17:00:15.439369Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"df.columns","metadata":{"execution":{"iopub.status.busy":"2023-10-14T17:00:15.441964Z","iopub.execute_input":"2023-10-14T17:00:15.442271Z","iopub.status.idle":"2023-10-14T17:00:15.450013Z","shell.execute_reply.started":"2023-10-14T17:00:15.442244Z","shell.execute_reply":"2023-10-14T17:00:15.448779Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"Index(['channelGrouping', 'date', 'fullVisitorId', 'sessionId',\n 'socialEngagementType', 'visitId', 'visitNumber', 'visitStartTime',\n 'device.browser', 'device.browserVersion', 'device.browserSize',\n 'device.operatingSystem', 'device.operatingSystemVersion',\n 'device.isMobile', 'device.mobileDeviceBranding',\n 'device.mobileDeviceModel', 'device.mobileInputSelector',\n 'device.mobileDeviceInfo', 'device.mobileDeviceMarketingName',\n 'device.flashVersion', 'device.language', 'device.screenColors',\n 'device.screenResolution', 'device.deviceCategory',\n 'geoNetwork.continent', 'geoNetwork.subContinent', 'geoNetwork.country',\n 'geoNetwork.region', 'geoNetwork.metro', 'geoNetwork.city',\n 'geoNetwork.cityId', 'geoNetwork.networkDomain', 'geoNetwork.latitude',\n 'geoNetwork.longitude', 'geoNetwork.networkLocation', 'totals.visits',\n 'totals.hits', 'totals.pageviews', 'totals.bounces', 'totals.newVisits',\n 'totals.transactionRevenue', 'trafficSource.campaign',\n 'trafficSource.source', 'trafficSource.medium', 'trafficSource.keyword',\n 'trafficSource.adwordsClickInfo.criteriaParameters',\n 'trafficSource.isTrueDirect', 'trafficSource.referralPath',\n 'trafficSource.adwordsClickInfo.page',\n 'trafficSource.adwordsClickInfo.slot',\n 'trafficSource.adwordsClickInfo.gclId',\n 'trafficSource.adwordsClickInfo.adNetworkType',\n 'trafficSource.adwordsClickInfo.isVideoAd', 'is_mobile',\n 'from_(not set)', 'from_Africa', 'from_Americas', 'from_Asia',\n 'from_Europe', 'from_Oceania', 'is_from_social', 'is_from_direct',\n 'normalized_hits', 'normalized_pageviews', 'device_weight',\n 'continent_weight', 'channel_weight', 'geoNetwork.continent_Africa',\n 'geoNetwork.continent_Americas', 'geoNetwork.continent_Asia',\n 'geoNetwork.continent_Europe', 'geoNetwork.continent_Oceania'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"markdown","source":"## Data Modeling","metadata":{}},{"cell_type":"markdown","source":"First, we try to classify customers who are likely to make transaction by random forest and KNN. Since the majority of the customers do not make transaction, there are a lot of null values of totals.transactionRevenue. We define the null values as 0 -- no_transaction and 1 -- yes_transaction. We try different methods to handle the imbalance dataset.","metadata":{}},{"cell_type":"code","source":"# Define two groups: 0 -- no_transaction, 1 -- yes_transaction\n\ndf_rf[\"totals.transactionRevenue\"].loc[df_rf[\"totals.transactionRevenue\"] != 0] = 1","metadata":{"execution":{"iopub.status.busy":"2023-10-14T17:00:15.451542Z","iopub.execute_input":"2023-10-14T17:00:15.451925Z","iopub.status.idle":"2023-10-14T17:00:15.466313Z","shell.execute_reply.started":"2023-10-14T17:00:15.451894Z","shell.execute_reply":"2023-10-14T17:00:15.465249Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"# Removing missing data with missing values, as these three is only 2% of the data\n\ndf_rf = df_rf[df_rf['geoNetwork.region'] != \"not available in demo dataset\"]\ndf_rf = df_rf[df_rf['geoNetwork.country'] != \"not available in demo dataset\"]\ndf_rf = df_rf[df_rf['geoNetwork.city'] != \"not available in demo dataset\"]","metadata":{"execution":{"iopub.status.busy":"2023-10-14T17:00:15.46826Z","iopub.execute_input":"2023-10-14T17:00:15.469019Z","iopub.status.idle":"2023-10-14T17:00:18.918263Z","shell.execute_reply.started":"2023-10-14T17:00:15.46898Z","shell.execute_reply":"2023-10-14T17:00:18.917306Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"# Dummy encoding\n\ndf_dummies = pd.get_dummies(df, columns=['geoNetwork.region','device.operatingSystem'])\nx=df_dummies[['totals.pageviews','totals.hits','visitNumber',\"visitStartTime\", 'totals.newVisits','device.operatingSystem_Android','device.operatingSystem_Chrome OS','device.operatingSystem_Linux','device.operatingSystem_Macintosh','device.operatingSystem_Windows','device.operatingSystem_iOS','totals.pageviews','totals.newVisits','geoNetwork.region_Texas', 'geoNetwork.region_Tokyo', 'geoNetwork.region_Utah','geoNetwork.region_Victoria', 'geoNetwork.region_Vienna','geoNetwork.region_Virginia', 'geoNetwork.region_Washington', 'geoNetwork.region_Wisconsin', 'geoNetwork.region_Zulia', 'geoNetwork.region_Zurich']]\ny=df_dummies[\"totals.transactionRevenue\"]\ndel df_rf","metadata":{"execution":{"iopub.status.busy":"2023-10-14T17:00:18.919549Z","iopub.execute_input":"2023-10-14T17:00:18.919838Z","iopub.status.idle":"2023-10-14T17:00:25.093482Z","shell.execute_reply.started":"2023-10-14T17:00:18.919814Z","shell.execute_reply":"2023-10-14T17:00:25.09228Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"markdown","source":"Random Forest without any imbalance handeling methods","metadata":{}},{"cell_type":"code","source":"x_train,x_test,y_train,y_test = train_test_split(x,y, test_size=0.2, random_state=10)\nmodel0=RandomForestClassifier(n_estimators=40)\nmodel0.fit(x_train,y_train)\ny_pred_test = model0.predict(x_test)","metadata":{"execution":{"iopub.status.busy":"2023-10-14T17:00:25.095538Z","iopub.execute_input":"2023-10-14T17:00:25.096106Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"accuracy_score(y_test, y_pred_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"confusion_matrix(y_test, y_pred_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cm = confusion_matrix\ncm = confusion_matrix(y_test, y_pred_test, labels=model0.classes_)\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model0.classes_)\ndisp.plot()\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del model1","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Random forest with different training weights: 0.2-0 vs 0.8-1 (20-80 rule);0.012-0 vs 0.988 - 1 (rate of the dataset); 0.01-0 vs 0.99-1 (strictly bias to 1)","metadata":{}},{"cell_type":"code","source":"# 20-80 rule\n\nw1 = min(len(df[df['totals.transactionRevenue'] == 0]) / len(df), 0.8)\nw0 = 1- w1 #avg rate or 20 - 80 rule\nmodel1=RandomForestClassifier(n_estimators=40,class_weight={0:w0, 1:w1})\nmodel1.fit(x_train,y_train)\ny_pred_test = model1.predict(x_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"accuracy_score(y_test, y_pred_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"confusion_matrix(y_test, y_pred_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"array([[77229, 475],\n [ 1005, 376]])\ncm = confusion_matrix\ncm = confusion_matrix(y_test, y_pred_test, labels=model1.classes_)\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model1.classes_)\ndisp.plot()\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del model1","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Rate of the dataset\n\nw1 = len(df[df['totals.transactionRevenue'] == 0]) / len(df)\nw0 = 1- w1\nmodel2=RandomForestClassifier(n_estimators=40,class_weight={0:w0, 1:w1})\nmodel2.fit(x_train,y_train)\ny_pred_test = model2.predict(x_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"accuracy_score(y_test, y_pred_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"confusion_matrix(y_test, y_pred_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cm = confusion_matrix\ncm = confusion_matrix(y_test, y_pred_test, labels=model2.classes_)\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model2.classes_)\ndisp.plot()\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del model2","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Extermely strict bias on 1\n\nmodel3=RandomForestClassifier(n_estimators=40,class_weight={0:0.01, 1:0.99})\nmodel3.fit(x_train,y_train)\ny_pred_test = model3.predict(x_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"accuracy_score(y_test, y_pred_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"confusion_matrix(y_test, y_pred_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cm = confusion_matrix\ncm = confusion_matrix(y_test, y_pred_test, labels=model3.classes_)\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model3.classes_)\ndisp.plot()\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del model3","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Next, we try shuffling the train test set based on y and train on pervious weights.","metadata":{}},{"cell_type":"code","source":"# Shuffling the train test set.\n\nx_train,x_test,y_train,y_test = train_test_split(x,y, test_size=0.2, random_state=10, stratify=y)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# 20-80 rule\n\nmodel4=RandomForestClassifier(n_estimators=40,class_weight={0:0.2, 1:0.8})\nmodel4.fit(x_train,y_train)\ny_pred_test = model4.predict(x_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"accuracy_score(y_test, y_pred_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"confusion_matrix(y_test, y_pred_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cm = confusion_matrix\ncm = confusion_matrix(y_test, y_pred_test, labels=model4.classes_)\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model4.classes_)\ndisp.plot()\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del model4","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Dataset rate\n\nw1 = len(df[df['totals.transactionRevenue'] == 0]) / len(df)\nw0 = 1- w1\nmodel5=RandomForestClassifier(n_estimators=40,class_weight={0:w0, 1:w1})\nmodel5.fit(x_train,y_train)\ny_pred_test = model5.predict(x_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"accuracy_score(y_test, y_pred_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"confusion_matrix(y_test, y_pred_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cm = confusion_matrix\ncm = confusion_matrix(y_test, y_pred_test, labels=model5.classes_)\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model5.classes_)\ndisp.plot()\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del model5","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Extereme strict bias on 1\n\nmodel6=RandomForestClassifier(n_estimators=40,class_weight={0:0.01, 1:0.99})\nmodel6.fit(x_train,y_train)\ny_pred_test = model6.predict(x_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"accuracy_score(y_test, y_pred_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"confusion_matrix(y_test, y_pred_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cm = confusion_matrix\ncm = confusion_matrix(y_test, y_pred_test, labels=model6.classes_)\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model5.classes_)\ndisp.plot()\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del model6","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Using undersampling to define the train set","metadata":{}},{"cell_type":"code","source":"from imblearn.under_sampling import RandomUnderSampler\nrus = RandomUnderSampler(random_state=42)\nX_res, y_res = rus.fit_resample(x_train,y_train)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_res","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model7=RandomForestClassifier(n_estimators=40)\nmodel7.fit(X_res, y_res)\ny_pred_test = model7.predict(x_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"accuracy_score(y_test, y_pred_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mean_squared_error(y_test, y_pred_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"confusion_matrix(y_test, y_pred_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cm = confusion_matrix\ncm = confusion_matrix(y_test, y_pred_test, labels=model7.classes_)\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model7.classes_)\ndisp.plot()\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Since we are focusing on taking actions on acquiring the potential customers, we want our prediction 1 as many and as accuary as possible. The undersampling provides best results on this so we decide to use random forest with undersampling. Next we also try different models.","metadata":{}},{"cell_type":"code","source":"# Determine numerical features\nnum_features = df.select_dtypes(include=['float64', 'int64']).columns.tolist()\n\n# Determine categorical features\ncat_features = df.select_dtypes(include=['object', 'category']).columns.tolist()\n\nprint(\"Numerical Features:\", num_features)\nprint(\"Categorical Features:\", cat_features)\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.preprocessing import StandardScaler, OneHotEncoder\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import LabelEncoder\n\ndf1=df\ndf['date'] = df['date'].astype(int)\ndf['purchase'] = (df['totals.transactionRevenue'] > 0).astype(int)\n# Apply label encoding\nle = LabelEncoder()\nfor col in cat_features:\n df[col] = le.fit_transform(df[col])\n\n# Separate features and target\nX = df.drop('purchase', axis=1)\ny = df['purchase']\n\nnull_percentage_feature = pd.DataFrame()\nfor col in df.columns:\n if df[col].isnull().sum() > 0:\n null_percentage_feature.loc[col,'NullPercentage'] = (df[col].isnull().sum())/len(df) * 100 \nprint(null_percentage_feature)\n\nfrom sklearn.impute import SimpleImputer\n\n# Numerical imputer\n# Impute numerical features with 0\nnum_imputer = SimpleImputer(strategy='constant', fill_value=0)\nX[num_features] = num_imputer.fit_transform(X[num_features])\n\n# Replace the NaN value in numerical features with 0\nX[num_features] = X[num_features].fillna(0)\n\n# Categorical imputer\ncat_imputer = SimpleImputer(strategy='most_frequent')\nX[cat_features] = cat_imputer.fit_transform(X[cat_features])\n\nnull_percentage_feature1 = pd.DataFrame()\nfor col in X.columns:\n if X[col].isnull().sum() > 0:\n null_percentage_feature1.loc[col,'NullPercentage'] = (X[col].isnull().sum())/len(X) * 100 \nprint(null_percentage_feature1)\n\n# Split data into training and testing sets\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**logistic regression & KNN**\n\nSimple Version:\nKNN runs for a very very long time","metadata":{}},{"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import accuracy_score\n\n# Logistic Regression\nlogistic_model = LogisticRegression()\nlogistic_model.fit(X_train, y_train)\ny_pred_logistic = logistic_model.predict(X_test)\naccuracy = accuracy_score(y_test, y_pred_logistic)\nprint(f\"Accuracy of Logistic Regression: {accuracy}\")\n\n# K-Nearest Neighbors (KNN)\n# knn = KNeighborsClassifier(n_neighbors=5)\n# knn.fit(X_train, y_train)\n# y_pred_knn = knn.predict(X_test)\n# accuracy_knn = accuracy_score(y_test, y_pred_knn)\n# print(f\"Accuracy of KNN: {accuracy_knn}\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Accuracy of Logistic Regression: 0.9874343637782118\nAccuracy of KNN: 0.9874343637782118","metadata":{}},{"cell_type":"markdown","source":"**Now to improve the accuracy and also the time efficiency of running model, we'd like to perform feature scaling and Reducing Dimensionality using PCA**\n\nKNN relies on the scale of its features.","metadata":{}},{"cell_type":"code","source":"df=df1\ndf['date'] = df['date'].astype(int)\ndf['purchase'] = (df['totals.transactionRevenue'] > 0).astype(int)\n\nscaler = StandardScaler()\ndf_scaled = df.copy()\n\n# Only scale the numerical features\ndf_scaled[num_features] = scaler.fit_transform(df[num_features])\n\n# Apply label encoding\nle = LabelEncoder()\nfor col in cat_features:\n df_scaled[col] = le.fit_transform(df_scaled[col])\n\n# Separate features and target\nX = df_scaled.drop('purchase', axis=1)\ny = df_scaled['purchase']\n\nfrom sklearn.impute import SimpleImputer\n\n# Numerical imputer\n# Impute numerical features with 0\nnum_imputer = SimpleImputer(strategy='constant', fill_value=0)\nX[num_features] = num_imputer.fit_transform(X[num_features])\n\n# Categorical imputer\ncat_imputer = SimpleImputer(strategy='most_frequent')\nX[cat_features] = cat_imputer.fit_transform(X[cat_features])\n\n\n# Split data into training and testing sets\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Feature Scaling Logistic & KNN**","metadata":{}},{"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import accuracy_score\n\n# Logistic Regression\nlogistic_model = LogisticRegression()\nlogistic_model.fit(X_train, y_train)\ny_pred_logistic = logistic_model.predict(X_test)\naccuracy = accuracy_score(y_test, y_pred_logistic)\nprint(f\"Accuracy of Logistic Regression: {accuracy}\")\n\n# K-Nearest Neighbors (KNN)\n# knn = KNeighborsClassifier(n_neighbors=5)\n# knn.fit(X_train, y_train)\n# y_pred_knn = knn.predict(X_test)\n# accuracy_knn = accuracy_score(y_test, y_pred_knn)\n# print(f\"Accuracy of KNN: {accuracy_knn}\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Accuracy of Logistic Regression: 0.9874343637782118\nAccuracy of KNN: 0.9874343637782118","metadata":{}},{"cell_type":"markdown","source":"**Reduce Dimensionality using PCA**","metadata":{}},{"cell_type":"code","source":"from sklearn.decomposition import PCA\n\ndf = df1\n\ndf['date'] = df['date'].astype(int)\ndf['purchase'] = (df['totals.transactionRevenue'] > 0).astype(int)\n\nscaler = StandardScaler()\ndf_scaled = df.copy()\n\n# Separate features and target\ndf_scaled = df_scaled.drop('purchase', axis=1)\n\n# Only scale the numerical features\ndf_scaled[num_features] = scaler.fit_transform(df[num_features])\n\n# Apply label encoding\nle = LabelEncoder()\nfor col in cat_features:\n df_scaled[col] = le.fit_transform(df_scaled[col])\n \n# Numerical imputer\n# Impute numerical features with 0\nnum_imputer = SimpleImputer(strategy='constant', fill_value=0)\ndf_scaled[num_features] = num_imputer.fit_transform(df_scaled[num_features])\n\n# Categorical imputer\ncat_imputer = SimpleImputer(strategy='most_frequent')\ndf_scaled[cat_features] = cat_imputer.fit_transform(df_scaled[cat_features])\n \n# Let's retain enough components to explain 95% of the variance\npca = PCA(n_components=0.95)\ndf_pca = pd.DataFrame(pca.fit_transform(df_scaled))\n\n# This will give you the number of components used to explain 95% of the variance\nprint(\"Number of PCA components:\", pca.n_components_)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import accuracy_score, classification_report\n\n# Assuming target is stored in a series called 'purchase'\n#X = df_pca\n#y = df['purchase'] \n\n\n# Splitting data into training and testing sets (80% train, 20% test)\n#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# Training Logistic Regression\n#log_reg = LogisticRegression()\n#log_reg.fit(X_train, y_train)\n\n# Predict using Logistic Regression\n#y_pred_logistic = log_reg.predict(X_test)\n\n# Evaluating Logistic Regression\n#accuracy_logistic = accuracy_score(y_test, y_pred_logistic)\n#print(\"Logistic Regression Accuracy:\", accuracy_logistic)\n#print(\"Classification Report for Logistic Regression:\")\n#print(classification_report(y_test, y_pred_logistic))\n\n# Training KNN\n#knn = KNeighborsClassifier(n_neighbors=5) # using 5 neighbors, but you can adjust this\n#knn.fit(X_train, y_train)\n\n# Predict using KNN\n#y_pred_knn = knn.predict(X_test)\n\n# Evaluating KNN\n#accuracy_knn = accuracy_score(y_test, y_pred_knn)\n#print(\"\\nKNN Accuracy:\", accuracy_knn)\n#print(\"Classification Report for KNN:\")\n#print(classification_report(y_test, y_pred_knn))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Visualized the Accuracy of Classification Model**","metadata":{}},{"cell_type":"markdown","source":"**CONFUSION MATRIX**","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.metrics import confusion_matrix\n\n#cm = confusion_matrix(y_test, y_pred_knn)\n\n#plt.figure(figsize=(8, 6))\n#sns.heatmap(cm, annot=True, fmt='g', cmap='Blues')\n#plt.title('Confusion Matrix')\n#plt.xlabel('Predicted labels')\n#plt.ylabel('True labels')\n#plt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Now we find out the problem. Although the accuracy rate is almost 98%, the predictive model predict all the values as 0, which ignores all the porpotion that customer would spend on purchasing","metadata":{}},{"cell_type":"markdown","source":"**KNN USING DIFFERENT VALUES OF n_neighbors**","metadata":{}},{"cell_type":"code","source":"# Training KNN n= 4\nknn = KNeighborsClassifier(n_neighbors=4) \nknn.fit(X_train, y_train)\n\n# Predict using KNN\ny_pred_knn4 = knn.predict(X_test)\n\n# Evaluating KNN\naccuracy_knn4 = accuracy_score(y_test, y_pred_knn4)\nprint(\"\\nKNN Accuracy4:\", accuracy_knn4)\nprint(\"Classification Report for KNN4:\")\nprint(classification_report(y_test, y_pred_knn4))\n\n# Training KNN n= 3\nknn3 = KNeighborsClassifier(n_neighbors=3) \nknn.fit(X_train, y_train)\n\n# Predict using KNN\ny_pred_knn3 = knn.predict(X_test)\n\n# Evaluating KNN\naccuracy_knn3 = accuracy_score(y_test, y_pred_knn3)\nprint(\"\\nKNN Accuracy3:\", accuracy_knn3)\nprint(\"Classification Report for KNN3:\")\nprint(classification_report(y_test, y_pred_knn3))\n\n# Training KNN n= 2\nknn = KNeighborsClassifier(n_neighbors=2) \nknn.fit(X_train, y_train)\n\n# Predict using KNN\ny_pred_knn2 = knn.predict(X_test)\n\n# Evaluating KNN\naccuracy_knn2 = accuracy_score(y_test, y_pred_knn2)\nprint(\"\\nKNN Accuracy2:\", accuracy_knn2)\nprint(\"Classification Report for KNN2:\")\nprint(classification_report(y_test, y_pred_knn2))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Dummy confusion matrices for demonstration purposes\n#cm5 = confusion_matrix(y_test, y_pred_knn)\n#cm4 = confusion_matrix(y_test, y_pred_knn4)\n#cm3 = confusion_matrix(y_test, y_pred_knn3)\n#cm2 = confusion_matrix(y_test, y_pred_knn2)\n\n#cms = [cm5, cm4, cm3, cm2]\n\n#fig, axes = plt.subplots(2, 2, figsize=(10, 10))\n\n#for cm, ax in zip(cms, axes.ravel()):\n # sns.heatmap(cm, annot=True, fmt='g', ax=ax, cmap=\"YlGnBu\")\n # ax.set_xlabel('Predicted labels')\n # ax.set_ylabel('True labels')\n\n#plt.tight_layout()\n#plt.show()\n\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Training KNN n= 1\nknn = KNeighborsClassifier(n_neighbors=1) \nknn.fit(X_train, y_train)\n\n# Predict using KNN\ny_pred_knn1 = knn.predict(X_test)\n\n# Evaluating KNN\naccuracy_knn1 = accuracy_score(y_test, y_pred_knn1)\nprint(\"\\nKNN Accuracy1:\", accuracy_knn1)\nprint(\"Classification Report for KNN2:\")\nprint(classification_report(y_test, y_pred_knn1))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cm1 = confusion_matrix(y_test, y_pred_knn1)\n\nplt.figure(figsize=(8, 6))\nsns.heatmap(cm1, annot=True, fmt='g', cmap='Blues')\nplt.title('Confusion Matrix')\nplt.xlabel('Predicted labels')\nplt.ylabel('True labels')\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**ROC CURVE**","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import roc_curve, auc\n\ny_pred_prob = knn.predict_proba(X_test)[:, 1]\n# Compute ROC curve and ROC area\nfpr, tpr, _ = roc_curve(y_test, y_pred_prob) # y_pred_prob is the probability of the positive class\nroc_auc = auc(fpr, tpr)\n\nplt.figure(figsize=(8, 6))\nplt.plot(fpr, tpr, color='darkorange', label='ROC curve (area = %0.2f)' % roc_auc)\nplt.plot([0, 1], [0, 1], color='navy', linestyle='--')\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver Operating Characteristic (ROC) Curve')\nplt.legend(loc=\"lower right\")\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Random Forest Classification: Randy","metadata":{}},{"cell_type":"code","source":"# Splitting the dataframe based on the condition\n\ndf=df1\ndf['date'] = df['date'].astype('int64')\ndf['purchase'] = (df['totals.transactionRevenue'] > 0).astype(int)\n\nscaler = StandardScaler()\ndf_scaled = df.copy()\n\n# Only scale the numerical features\ndf_scaled[num_features] = scaler.fit_transform(df[num_features])\n\n# Apply label encoding\nle = LabelEncoder()\nfor col in cat_features:\n df_scaled[col] = le.fit_transform(df_scaled[col])\n \n# Numerical imputer\n# Impute numerical features with 0\nnum_imputer = SimpleImputer(strategy='constant', fill_value=0)\ndf_scaled[num_features] = num_imputer.fit_transform(df_scaled[num_features])\n\n# Categorical imputer\ncat_imputer = SimpleImputer(strategy='most_frequent')\ndf_scaled[cat_features] = cat_imputer.fit_transform(df_scaled[cat_features])\n\ndf_positive_revenue = df_scaled[df_scaled['totals.transactionRevenue'] > 0].drop('purchase', axis=1)\ndf_zero_or_negative_revenue = df_scaled[df_scaled['totals.transactionRevenue'] <= 0]\n\n# Drop the specified columns from your DataFrame (df)\ncolumns_to_drop = [\n 'fullVisitorId', 'sessionId', 'socialEngagementType', 'device.browserVersion', \n 'date'\n]\ndf_positive_revenue.drop(columns=columns_to_drop, inplace=True)\n\ndf_positive_revenue","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Count zeros in each column\nzero_counts = df_positive_revenue.eq(0).sum()\n\n# Calculate percentages\nzero_percentages = (zero_counts / len(df_positive_revenue)) * 100\n\n# Display the results\nprint(\"Number of Zeros in Each Column:\")\nprint(zero_counts)\nprint(\"\\nPercentage of Zeros in Each Column:\")\nprint(zero_percentages)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Calculate mean and standard deviation\nrevenue_mean = df_positive_revenue['totals.transactionRevenue'].mean()\nrevenue_std = df_positive_revenue['totals.transactionRevenue'].std()\n\n# Plot the distribution\nplt.figure(figsize=(10, 6))\nsns.histplot(df_positive_revenue['totals.transactionRevenue'], kde=True)\nplt.xlabel('Transaction Revenue')\nplt.ylabel('Frequency')\nplt.title(f'Distribution of Transaction Revenue\\nMean: {revenue_mean:.2f}, Std: {revenue_std:.2f}')\nplt.show()\n\n# Display mean and standard deviation\nprint(f\"Mean of 'totals.transactionRevenue': {revenue_mean:.2f}\")\nprint(f\"Standard Deviation of 'totals.transactionRevenue': {revenue_std:.2f}\")\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np\n\n# Add 1 to avoid taking log of zero\ndf_positive_revenue['log_transactionRevenue'] = np.log1p(df_positive_revenue['totals.transactionRevenue'])\n# Set the style for the seaborn plot\nsns.set_style(\"whitegrid\")\n\n# Plotting the distribution\nplt.figure(figsize=(12, 6))\nsns.histplot(data=df_positive_revenue, x='log_transactionRevenue', kde=True, bins=50)\nplt.title('Distribution of Log-transformed Transaction Revenue')\nplt.xlabel('Log-transformed Transaction Revenue')\nplt.ylabel('Frequency')\nplt.show()\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from scipy.stats import boxcox\n\n# Ensure data is positive; you might want to add a constant if there are zeros.\ntransformed, lambda_value = boxcox(df_positive_revenue['totals.transactionRevenue'] + 1)\ndf_positive_revenue['boxcox_transactionRevenue'] = transformed\nsns.set_style(\"whitegrid\")\n\n# Plotting the distribution\nplt.figure(figsize=(12, 6))\nsns.histplot(data=df_positive_revenue, x='boxcox_transactionRevenue', kde=True, bins=50)\nplt.title('Distribution of boxcox_transactionRevenue')\nplt.xlabel('boxcox_transactionRevenue Transaction Revenue')\nplt.ylabel('Frequency')\nplt.show()\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.preprocessing import QuantileTransformer\n\nqt = QuantileTransformer(output_distribution='normal')\ndf_positive_revenue['quantile_transformed_Revenue'] = qt.fit_transform(df_positive_revenue[['totals.transactionRevenue']])\n\n# Set the style for the seaborn plot\nsns.set_style(\"whitegrid\")\n\n# Plotting the distribution\nplt.figure(figsize=(12, 6))\nsns.histplot(data=df_positive_revenue, x='quantile_transformed_Revenue', kde=True, bins=50)\nplt.title('Distribution of quantile_transformed_Revenue')\nplt.xlabel('quantile_transformed_Revenue Transaction Revenue')\nplt.ylabel('Frequency')\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"now we would based on quantile_transformed_revenue to perform our result","metadata":{}},{"cell_type":"code","source":"df_positive_revenue = df_positive_revenue.drop(['log_transactionRevenue','boxcox_transactionRevenue','quantile_transformed_Revenue'], axis=1)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"correlation_matrix = df_positive_revenue.corr()\ncorrelation_with_target = correlation_matrix[\"totals.transactionRevenue\"].sort_values(ascending=False)\n\nweak_correlated_features = correlation_with_target[(correlation_with_target > -0.05) & (correlation_with_target < 0.05)].index\ndf_positive_revenue = df_positive_revenue.drop(columns=weak_correlated_features)\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Spliting the data**","metadata":{}},{"cell_type":"code","source":"X = df_positive_revenue.drop('totals.transactionRevenue', axis=1)\ny = df_positive_revenue['totals.transactionRevenue']\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Linear regression with Lasso**","metadata":{}},{"cell_type":"code","source":"from sklearn.linear_model import Lasso\nfrom sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n\nlasso = Lasso()\nlasso.fit(X_train, y_train)\ny_pred_lasso = lasso.predict(X_test)\n\nprint(\"Lasso Regression:\")\nprint(\"R2 Score:\", r2_score(y_test, y_pred_lasso))\nprint(\"MSE:\", mean_squared_error(y_test, y_pred_lasso))\nprint(\"MAE:\", mean_absolute_error(y_test, y_pred_lasso))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**CART(decision tree)**","metadata":{}},{"cell_type":"code","source":"from sklearn.tree import DecisionTreeRegressor\n\ncart = DecisionTreeRegressor()\ncart.fit(X_train, y_train)\ny_pred_cart = cart.predict(X_test)\n\nprint(\"\\nDecision Trees:\")\nprint(\"R2 Score:\", r2_score(y_test, y_pred_cart))\nprint(\"MSE:\", mean_squared_error(y_test, y_pred_cart))\nprint(\"MAE:\", mean_absolute_error(y_test, y_pred_cart))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Random Forest**","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestRegressor\n\nrf = RandomForestRegressor(n_estimators=250, random_state=42)\nrf.fit(X_train, y_train)\ny_pred_rf = rf.predict(X_test)\n\nprint(\"\\nRandom Forest:\")\nprint(\"R2 Score:\", r2_score(y_test, y_pred_rf))\nprint(\"MSE:\", mean_squared_error(y_test, y_pred_rf))\nprint(\"MAE:\", mean_absolute_error(y_test, y_pred_rf))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Neural Network**","metadata":{}},{"cell_type":"code","source":"from sklearn.neural_network import MLPRegressor\n\nnn = MLPRegressor(max_iter=1000)\nnn.fit(X_train, y_train)\ny_pred_nn = nn.predict(X_test)\n\nprint(\"\\nNeural Networks:\")\nprint(\"R2 Score:\", r2_score(y_test, y_pred_nn))\nprint(\"MSE:\", mean_squared_error(y_test, y_pred_nn))\nprint(\"MAE:\", mean_absolute_error(y_test, y_pred_nn))\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(np.isnan(X_train).any(), np.isnan(y_train).any())","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train = y_train.to_frame()\ny_train\ny_test = y_test.to_frame()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(X_train.dtypes)\n# For X_train\nint_columns_train = X_train.select_dtypes(include=['int64']).columns\nX_train[int_columns_train] = X_train[int_columns_train].astype(float)\nint_columns_ytrain = y_train.select_dtypes(include=['int64']).columns\ny_train[int_columns_ytrain] = y_train[int_columns_ytrain].astype(float)\n\n# For X_test\nint_columns_test = X_test.select_dtypes(include=['int64']).columns\nX_test[int_columns_test] = X_test[int_columns_test].astype(float)\nint_columns_ytest = y_test.select_dtypes(include=['int64']).columns\ny_test[int_columns_ytest] = y_test[int_columns_ytest].astype(float)\n\nprint(X_train.dtypes)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"NaN in X_train:\", X_train.isnull().sum().sum())\nprint(\"Infinity in X_train:\", np.isinf(X_train).sum().sum())\nprint(\"NaN in y_train:\", y_train.isnull().sum().sum())\nprint(\"Infinity in y_train:\", np.isinf(y_train).sum().sum())","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"X_train shape:\", X_train.shape)\nprint(\"y_train shape:\", y_train.shape)\nprint(\"X_test shape:\", X_test.shape)\nprint(\"y_test shape:\", y_test.shape)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_test","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(X_train.max(), X_train.min(), y_train.max(), y_train.min())","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.preprocessing import StandardScaler\n\n# Initialize the scaler\nscaler = StandardScaler()\n\n# Fit the scaler to the training data only\nX_train_normalized = scaler.fit_transform(X_train)\n\n# Use this scaler to transform the test data\nX_test_normalized = scaler.transform(X_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Normarlization must be perform for there is extreme value, which will cause our neural network model result into NAN**","metadata":{}},{"cell_type":"markdown","source":"**plot the training of neural network**","metadata":{}},{"cell_type":"code","source":"from keras.regularizers import l1, l2, l1_l2\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.layers import Dense, Dropout\nfrom tensorflow.keras.regularizers import l2\n\n\nX_train_normalized = np.array(X_train_normalized).astype(np.float32)\ny_train = np.array(y_train).astype(np.float32)\nX_test_normalized = np.array(X_test_normalized).astype(np.float32)\ny_test = np.array(y_test).astype(np.float32)\n\n# Neural Network model\nmodel = Sequential()\n\n# Input layer\nmodel.add(Dense(24, input_dim=X_train_normalized.shape[1], activation='relu', kernel_initializer='he_normal', kernel_regularizer=l2(0.01)))\nmodel.add(Dropout(0.3))\n# Hidden layers\nmodel.add(Dense(22, activation='relu', kernel_regularizer=l2(0.01)))\nmodel.add(Dropout(0.3))\nmodel.add(Dense(16, activation='relu',kernel_regularizer=l2(0.01)))\nmodel.add(Dropout(0.2))\n\n# Output layer\nmodel.add(Dense(1, activation='linear')) # 'linear' activation for regression tasks\n\n# Compile the model\noptimizer = Adam(learning_rate=0.0001)\nmodel.compile(loss='mean_squared_error', optimizer=optimizer, metrics=['mean_absolute_error', 'mean_squared_error'])\n\n\nfrom keras.callbacks import EarlyStopping\nearly_stop = EarlyStopping(monitor='val_loss', patience=10) # stops after 10 epochs with no improvement\n\n# Train the model\nhistory = model.fit(X_train_normalized, y_train, validation_data=(X_test_normalized, y_test), epochs=75, batch_size=32, callbacks=[early_stop]) \n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Plot training & validation loss values\nplt.figure(figsize=(12, 6))\nplt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.title('Model Loss')\nplt.ylabel('Loss')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Validation'], loc='upper right')\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"loss, mae, mse = model.evaluate(X_test_normalized, y_test, verbose=0)\n\nprint(f\"Mean Abs Error on Test Data: {mae:.2f}\")\nprint(f\"Mean Squared Error on Test Data: {mse:.2f}\")\nprint(f\"Loss on Test Data: {loss:.2f}\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Plot training & validation mean absolute error values\nplt.figure(figsize=(12, 6))\nplt.plot(history.history['mean_squared_error'])\nplt.plot(history.history['val_mean_squared_error'])\nplt.title('Model Mean Squared Error')\nplt.ylabel('Mean Squared Error')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Validation'], loc='upper right')\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred_nn_re = model.predict(X_test_normalized)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\n\n# Define a function to calculate metrics for a model\ndef calculate_metrics(y_true, y_pred):\n return [\n r2_score(y_true, y_pred),\n mean_squared_error(y_true, y_pred),\n mean_absolute_error(y_true, y_pred)\n ]\n\n# Calculate metrics for each model\nmetrics_lasso = calculate_metrics(y_test, y_pred_lasso)\nmetrics_cart = calculate_metrics(y_test, y_pred_cart)\nmetrics_rf = calculate_metrics(y_test, y_pred_rf)\nmetrics_nn = calculate_metrics(y_test, y_pred_nn_re)\n\n# Create a dataframe to store the metrics\ndf_summary = pd.DataFrame({\n 'Model': ['Lasso Regression', 'Decision Trees', 'Random Forest', 'Neural Networks'],\n 'R2 Score': [metrics_lasso[0], metrics_cart[0], metrics_rf[0], metrics_nn[0]],\n 'MSE': [metrics_lasso[1], metrics_cart[1], metrics_rf[1], metrics_nn[1]],\n 'MAE': [metrics_lasso[2], metrics_cart[2], metrics_rf[2], metrics_nn[2]]\n})\n\n# Display the summary table\nprint(df_summary)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Plotting training and validation loss\nplt.plot(history.history['loss'], label='Training Loss')\nplt.plot(history.history['val_loss'], label='Validation Loss')\nplt.legend()\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"A big gap between training and validation curves indicates overfitting.\n\nIncrease dropout in the neural network.\nAdd regularization (e.g., L1/L2 regularization).\nReduce the complexity of the model (e.g., fewer layers/neurons).","metadata":{}},{"cell_type":"markdown","source":"1. Hyperparameter Tuning:\nFor hyperparameter tuning, one of the most common methods is Grid Search combined with Cross-Validation.","metadata":{}},{"cell_type":"markdown","source":"a. lasso","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import GridSearchCV\nfrom sklearn.linear_model import Lasso\n\n# Define hyperparameters to tune\nparam_grid_lasso = {'alpha': [1e-4, 1e-3, 1e-2, 1e-1, 1, 10, 100]}\n\ngrid_lasso = GridSearchCV(Lasso(), param_grid_lasso, cv=5, scoring='neg_mean_squared_error')\ngrid_lasso.fit(X_train_normalized, y_train)\n\n# Best hyperparameters\ngrid_lasso.best_params_\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"b.rf","metadata":{}},{"cell_type":"markdown","source":"1. Cross-Validation:\nTo perform K-Fold cross-validation and capture the scores:","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import cross_val_score\nfrom sklearn.linear_model import Lasso\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.tree import DecisionTreeRegressor\n\n# Lasso\nlasso_model = Lasso(alpha=grid_lasso.best_params_['alpha']) # Assuming you've already done grid search\nlasso_scores = cross_val_score(lasso_model, X_train_normalized, y_train, cv=5)\nlasso_scores_mean = np.mean(lasso_scores)\n\n# Random Forest\nrf_model = RandomForestRegressor(n_estimators=250, \n max_depth=20, \n min_samples_split=5)\nrf_scores = cross_val_score(rf_model, X_train_normalized, y_train, cv=5)\nrf_scores_mean = np.mean(rf_scores)\n\n# CART (Decision Trees)\ncart_model = DecisionTreeRegressor()\ncart_scores = cross_val_score(cart_model, X_train_normalized, y_train, cv=5)\ncart_scores_mean = np.mean(cart_scores)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from keras.wrappers.scikit_learn import KerasRegressor\nfrom sklearn.model_selection import KFold\n\n# Create a function to return the model\ndef create_model():\n model = Sequential()\n model.add(Dense(71, input_dim=X_train_normalized.shape[1], activation='relu', kernel_initializer='he_normal'))\n model.add(Dense(64, activation='relu'))\n model.add(Dropout(0.5))\n model.add(Dense(32, activation='relu'))\n model.add(Dropout(0.5))\n model.add(Dense(1, activation='linear'))\n model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mean_absolute_error','mean_squared_error'])\n return model\n\nmodel = KerasRegressor(build_fn=create_model, epochs=50, batch_size=32, verbose=0)\nkfold = KFold(n_splits=5)\nnn_scores = cross_val_score(model, X_train_normalized, y_train, cv=kfold)\n\n# Cross-validation scores\nnn_scores_mean = np.mean(nn_scores)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"2. Visualization:\nLet's visualize the results of the CV for each model:","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import mean_squared_error, mean_absolute_error\nfrom sklearn.model_selection import cross_val_predict, KFold\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Define a custom scorer for MAPE\ndef mape_scorer(y_true, y_pred):\n return np.mean(np.abs((y_true - y_pred) / y_true)) *100\n\n# Initialize lists to store MSE and MAE values for each model\nmse_scores = {\"Lasso\": [], \"Random Forest\": [], \"CART\": [], \"Neural Network\": []}\nmae_scores = {\"Lasso\": [], \"Random Forest\": [], \"CART\": [], \"Neural Network\": []}\nmape_scores = {\"Lasso\": [], \"Random Forest\": [], \"CART\": [], \"Neural Network\": []}\n\n# Define neural network model\ndef create_neural_network_model():\n model = Sequential()\n model.add(Dense(24, input_dim=X_train_normalized.shape[1], activation='relu', kernel_initializer='he_normal', kernel_regularizer=l2(0.01)))\n model.add(Dropout(0.3))\n model.add(Dense(22, activation='relu', kernel_regularizer=l2(0.01)))\n model.add(Dropout(0.3))\n model.add(Dense(16, activation='relu', kernel_regularizer=l2(0.01)))\n model.add(Dropout(0.2))\n model.add(Dense(1, activation='linear'))\n optimizer = Adam(learning_rate=0.0001)\n model.compile(loss='mean_squared_error', optimizer=optimizer, metrics=['mean_absolute_error', 'mean_squared_error'])\n return model\n\n# Convert data to numpy arrays\nX_train_normalized = np.array(X_train_normalized).astype(np.float32)\ny_train = np.array(y_train).astype(np.float32)\n\n# Define the number of cross-validation folds\nn_folds = 5\nkf = KFold(n_splits=n_folds, shuffle=True, random_state=42)\n\n# Perform cross-validation\nfor train_idx, val_idx in kf.split(X_train_normalized):\n X_train_fold, X_val_fold = X_train_normalized[train_idx], X_train_normalized[val_idx]\n y_train_fold, y_val_fold = y_train[train_idx], y_train[val_idx]\n \n # Lasso\n lasso_model = Lasso(alpha=grid_lasso.best_params_['alpha'])\n lasso_predictions = cross_val_predict(lasso_model, X_train_fold, y_train_fold, cv=5)\n mse_scores[\"Lasso\"].append(mean_squared_error(y_train_fold, lasso_predictions))\n mae_scores[\"Lasso\"].append(mean_absolute_error(y_train_fold, lasso_predictions))\n mape_scores[\"Lasso\"].append(mape_scorer(y_train_fold, lasso_predictions))\n \n # Random Forest\n rf_model = RandomForestRegressor(n_estimators=250, max_depth=20, min_samples_split=5)\n rf_predictions = cross_val_predict(rf_model, X_train_fold, y_train_fold, cv=5)\n mse_scores[\"Random Forest\"].append(mean_squared_error(y_train_fold, rf_predictions))\n mae_scores[\"Random Forest\"].append(mean_absolute_error(y_train_fold, rf_predictions))\n mape_scores[\"Random Forest\"].append(mape_scorer(y_train_fold, rf_predictions))\n \n # CART (Decision Trees)\n cart_model = DecisionTreeRegressor()\n cart_predictions = cross_val_predict(cart_model, X_train_fold, y_train_fold, cv=5)\n mse_scores[\"CART\"].append(mean_squared_error(y_train_fold, cart_predictions))\n mae_scores[\"CART\"].append(mean_absolute_error(y_train_fold, cart_predictions))\n mape_scores[\"CART\"].append(mape_scorer(y_train_fold, cart_predictions))\n \n # Neural Network\n model = create_neural_network_model()\n early_stop = EarlyStopping(monitor='val_loss', patience=10)\n history = model.fit(X_train_fold, y_train_fold, validation_data=(X_val_fold, y_val_fold), epochs=75, batch_size=32, callbacks=[early_stop], verbose=0)\n nn_predictions = model.predict(X_val_fold)\n mse_scores[\"Neural Network\"].append(mean_squared_error(y_val_fold, nn_predictions))\n mae_scores[\"Neural Network\"].append(mean_absolute_error(y_val_fold, nn_predictions))\n mape_scores[\"Neural Network\"].append(mape_scorer(y_val_fold, nn_predictions))\n \n# Create DataFrames for MSE, MAE, and MAPE scores\nmse_df = pd.DataFrame(mse_scores)\nmae_df = pd.DataFrame(mae_scores)\nmape_df = pd.DataFrame(mape_scores)\n\n# Visualize the results\nplt.figure(figsize=(18, 6))\n\n# Bar plot for MSE\nplt.subplot(1, 3, 1)\nsns.barplot(data=mse_df)\nplt.title('Mean Squared Error (MSE) for Each Model')\n\n# Bar plot for MAE\nplt.subplot(1, 3, 2)\nsns.barplot(data=mae_df)\nplt.title('Mean Absolute Error (MAE) for Each Model')\n\n# Bar plot for MAPE\nplt.subplot(1, 3, 3)\nsns.barplot(data=mape_df)\nplt.title('Mean Absolute Percentage Error (MAPE) for Each Model')\n\nplt.tight_layout()\nplt.show()\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Create a dictionary to store the results\nresults = {\n 'Model': [],\n 'MSE Mean': [],\n 'MSE Values': [],\n 'MAE Mean': [],\n 'MAE Values': [],\n 'MAPE Mean': [],\n 'MAPE Values': []\n}\n\n# Populate the results dictionary\nfor model_name in mse_scores.keys():\n mse_mean = np.mean(mse_scores[model_name])\n mae_mean = np.mean(mae_scores[model_name])\n mape_mean = np.mean(mape_scores[model_name])\n mse_values = mse_scores[model_name]\n mae_values = mae_scores[model_name]\n mape_values = mape_scores[model_name]\n \n results['Model'].append(model_name)\n results['MSE Mean'].append(mse_mean)\n results['MSE Values'].append(', '.join(map(str, mse_values)))\n results['MAE Mean'].append(mae_mean)\n results['MAE Values'].append(', '.join(map(str, mae_values)))\n results['MAPE Mean'].append(mape_mean)\n results['MAPE Values'].append(', '.join(map(str, mape_values)))\n\n# Create a DataFrame from the results\nresults_df = pd.DataFrame(results)\n\n# Print the combined results table\nresults_df.round(2)\n\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"All the totalstranscationRevenues here are now quantile_tranformed_reveue","metadata":{}},{"cell_type":"code","source":"qt = QuantileTransformer(output_distribution='normal')\ndf_positive_revenue['totals.transactionRevenue'] = qt.fit_transform(df_positive_revenue[['totals.transactionRevenue']])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"correlation_matrix = df_positive_revenue.corr()\ncorrelation_with_target = correlation_matrix[\"totals.transactionRevenue\"].sort_values(ascending=False)\n\nweak_correlated_features = correlation_with_target[(correlation_with_target > -0.05) & (correlation_with_target < 0.05)].index\ndf_positive_revenue = df_positive_revenue.drop(columns=weak_correlated_features)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X = df_positive_revenue.drop('totals.transactionRevenue', axis=1)\ny = df_positive_revenue['totals.transactionRevenue']\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nfrom sklearn.preprocessing import StandardScaler\ny_train = y_train.to_frame()\ny_train\ny_test = y_test.to_frame()\n# Initialize the scaler\nscaler = StandardScaler()\n\n# Fit the scaler to the training data only\nX_train_normalized = scaler.fit_transform(X_train)\n\n# Use this scaler to transform the test data\nX_test_normalized = scaler.transform(X_test)\nX_train_normalized = np.array(X_train_normalized).astype(np.float32)\ny_train = np.array(y_train).astype(np.float32)\nX_test_normalized = np.array(X_test_normalized).astype(np.float32)\ny_test = np.array(y_test).astype(np.float32)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Define a custom scorer for MAPE\ndef mape_scorer(y_true, y_pred):\n return np.mean(np.abs((y_true - y_pred) / y_true)) *100\n\n# Initialize lists to store MSE and MAE values for each model\nmse_scores = {\"Lasso\": [], \"Random Forest\": [], \"CART\": [], \"Neural Network\": []}\nmae_scores = {\"Lasso\": [], \"Random Forest\": [], \"CART\": [], \"Neural Network\": []}\nmape_scores = {\"Lasso\": [], \"Random Forest\": [], \"CART\": [], \"Neural Network\": []}\n\n# Define neural network model\ndef create_neural_network_model():\n model = Sequential()\n model.add(Dense(39, input_dim=X_train_normalized.shape[1], activation='relu', kernel_initializer='he_normal', kernel_regularizer=l2(0.01)))\n model.add(Dropout(0.3))\n model.add(Dense(32, activation='relu', kernel_regularizer=l2(0.01)))\n model.add(Dropout(0.2))\n model.add(Dense(16, activation='relu', kernel_regularizer=l2(0.01)))\n model.add(Dropout(0.2))\n model.add(Dense(1, activation='linear'))\n optimizer = Adam(learning_rate=0.0001)\n model.compile(loss='mean_squared_error', optimizer=optimizer, metrics=['mean_absolute_error', 'mean_squared_error'])\n return model\n\n# Convert data to numpy arrays\nX_train_normalized = np.array(X_train_normalized).astype(np.float32)\ny_train = np.array(y_train).astype(np.float32)\n\n# Define the number of cross-validation folds\nn_folds = 5\nkf = KFold(n_splits=n_folds, shuffle=True, random_state=42)\n\n# Perform cross-validation\nfor train_idx, val_idx in kf.split(X_train_normalized):\n X_train_fold, X_val_fold = X_train_normalized[train_idx], X_train_normalized[val_idx]\n y_train_fold, y_val_fold = y_train[train_idx], y_train[val_idx]\n \n # Lasso\n lasso_model = Lasso(alpha=grid_lasso.best_params_['alpha'])\n lasso_predictions = cross_val_predict(lasso_model, X_train_fold, y_train_fold, cv=5)\n mse_scores[\"Lasso\"].append(mean_squared_error(y_train_fold, lasso_predictions))\n mae_scores[\"Lasso\"].append(mean_absolute_error(y_train_fold, lasso_predictions))\n mape_scores[\"Lasso\"].append(mape_scorer(y_train_fold, lasso_predictions))\n \n # Random Forest\n rf_model = RandomForestRegressor(n_estimators=250, max_depth=20, min_samples_split=5)\n rf_predictions = cross_val_predict(rf_model, X_train_fold, y_train_fold, cv=5)\n mse_scores[\"Random Forest\"].append(mean_squared_error(y_train_fold, rf_predictions))\n mae_scores[\"Random Forest\"].append(mean_absolute_error(y_train_fold, rf_predictions))\n mape_scores[\"Random Forest\"].append(mape_scorer(y_train_fold, rf_predictions))\n \n # CART (Decision Trees)\n cart_model = DecisionTreeRegressor()\n cart_predictions = cross_val_predict(cart_model, X_train_fold, y_train_fold, cv=5)\n mse_scores[\"CART\"].append(mean_squared_error(y_train_fold, cart_predictions))\n mae_scores[\"CART\"].append(mean_absolute_error(y_train_fold, cart_predictions))\n mape_scores[\"CART\"].append(mape_scorer(y_train_fold, cart_predictions))\n \n # Neural Network\n model = create_neural_network_model()\n early_stop = EarlyStopping(monitor='val_loss', patience=10)\n history = model.fit(X_train_fold, y_train_fold, validation_data=(X_val_fold, y_val_fold), epochs=75, batch_size=32, callbacks=[early_stop], verbose=0)\n nn_predictions = model.predict(X_val_fold)\n mse_scores[\"Neural Network\"].append(mean_squared_error(y_val_fold, nn_predictions))\n mae_scores[\"Neural Network\"].append(mean_absolute_error(y_val_fold, nn_predictions))\n mape_scores[\"Neural Network\"].append(mape_scorer(y_val_fold, nn_predictions))\n \n# Create DataFrames for MSE, MAE, and MAPE scores\nmse_df = pd.DataFrame(mse_scores)\nmae_df = pd.DataFrame(mae_scores)\nmape_df = pd.DataFrame(mape_scores)\n\n# Visualize the results\nplt.figure(figsize=(18, 6))\n\n# Bar plot for MSE\nplt.subplot(1, 3, 1)\nsns.barplot(data=mse_df)\nplt.title('Mean Squared Error (MSE) for Each Model')\n\n# Bar plot for MAE\nplt.subplot(1, 3, 2)\nsns.barplot(data=mae_df)\nplt.title('Mean Absolute Error (MAE) for Each Model')\n\n# Bar plot for MAPE\nplt.subplot(1, 3, 3)\nsns.barplot(data=mape_df)\nplt.title('Mean Absolute Percentage Error (MAPE) for Each Model')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Create a dictionary to store the results\nresults = {\n 'Model': [],\n 'MSE Mean': [],\n 'MSE Values': [],\n 'MAE Mean': [],\n 'MAE Values': [],\n 'MAPE Mean': [],\n 'MAPE Values': []\n}\n\n# Populate the results dictionary\nfor model_name in mse_scores.keys():\n mse_mean = np.mean(mse_scores[model_name])\n mae_mean = np.mean(mae_scores[model_name])\n mape_mean = np.mean(mape_scores[model_name])\n mse_values = mse_scores[model_name]\n mae_values = mae_scores[model_name]\n mape_values = mape_scores[model_name]\n \n results['Model'].append(model_name)\n results['MSE Mean'].append(mse_mean)\n results['MSE Values'].append(', '.join(map(str, mse_values)))\n results['MAE Mean'].append(mae_mean)\n results['MAE Values'].append(', '.join(map(str, mae_values)))\n results['MAPE Mean'].append(mape_mean)\n results['MAPE Values'].append(', '.join(map(str, mape_values)))\n\n# Create a DataFrame from the results\nresults_df = pd.DataFrame(results)\n\n# Print the combined results table\nresults_df.round(2)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_test = pd.DataFrame(y_test, columns=['totals.transactionRevenue'])\nX_test","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y=y_test['totals.transactionRevenue']\ny","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout\nfrom keras.optimizers import Adam\nfrom keras.regularizers import l2\nfrom sklearn.preprocessing import QuantileTransformer\nfrom scipy.stats import norm\nimport random\n\n\n\n\n# Train the Neural Network model on your data\nneural_network_model = create_neural_network_model()\nneural_network_model.fit(X_train_normalized, y_train, epochs=50, batch_size=32, validation_split=0.1, verbose=2)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Predict the totals.transactions.Revenue using the trained Neural Network model\npredicted_revenue = neural_network_model.predict(X_test_normalized)\n# Assuming quantile transformed predicted revenue is stored in `predicted_revenue`\nmean_revenue = np.mean(predicted_revenue)\nstd_revenue = np.std(predicted_revenue)\n# Setting threshold to be 1 standard deviation above the mean (adjust as necessary)\nthreshold = 0.9*predicted_revenue + 0.08*std_revenue\n\npredicted_revenue = qt.inverse_transform(predicted_revenue.reshape(-1, 1))\ny=y_test['totals.transactionRevenue']\ny_test['totals.transactionRevenue'] = qt.inverse_transform(y_test[['totals.transactionRevenue']])\nthreshold = pd.DataFrame(threshold, columns=['totals.transactionRevenue'])\nthreshold = qt.inverse_transform(threshold[['totals.transactionRevenue']])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_test\nX_test['totals.transactionRevenue'] = y_test['totals.transactionRevenue']\nX_test['totals.transactionRevenue']","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Calculate the cost\n \nX_test['threshold'] = threshold\n\nX_test['predicted_revenue'] = predicted_revenue\nX_test['predicted_purchase'] = np.where(predicted_revenue > threshold, 1, 0)\nX_test['totals.transactionRevenue'] = y_test['totals.transactionRevenue']\nX_test['totals.transactionRevenue'] = X_test['totals.transactionRevenue']\nX_test['cost'] = 0.05* X_test['totals.transactionRevenue']\nX_test = X_test.reset_index(drop=True)\n\nimport numpy as np\n\n# Apply log transformation\nX_test['log_transactionRevenue'] = np.log1p(X_test['totals.transactionRevenue'])\n\n# Drop non-finite values (like NaN or inf) \nlog_revenue_filtered = X_test['log_transactionRevenue'].dropna()\n\n# Ensure there are no infinite values\nlog_revenue_filtered = log_revenue_filtered.replace([np.inf, -np.inf], np.nan).dropna()\n\n# Fit a normal distribution to the log-transformed revenue\nmu, std = norm.fit(log_revenue_filtered)\n\n\n# Calculate probability, expected net gain, and select optimal k\ndef get_optimal_k(row):\n ks = np.linspace(0, 2*std, 500) # Range of k values\n probs = 1*[1 - norm.cdf(np.log1p(row['totals.transactionRevenue'] + k), mu, std) for k in ks]\n expected_revenue_increase = [prob * (row['predicted_revenue'] + k) for prob, k in zip(probs, ks)]\n net_gains = [rev - row['cost'] for rev in expected_revenue_increase]\n return ks[np.argmax(net_gains)]\n\nX_test['optimal_k'] = X_test.apply(get_optimal_k, axis=1)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_test['optimal_k']","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Visualization\n# For one customer\ncustomer_id = 10 # example\nks = np.linspace(0, 2*std, 100)\nprobs = [1 - norm.cdf(np.log1p(X_test.loc[customer_id, 'predicted_revenue'] + k), mu, std) for k in ks]\nexpected_revenue_increase = [prob * (X_test.loc[customer_id, 'predicted_revenue'] + k) for prob, k in zip(probs, ks)]\nnet_gains = [rev - X_test.loc[customer_id, 'cost'] for rev in expected_revenue_increase]\n\nplt.figure()\nplt.plot(ks, net_gains)\nplt.xlabel('k (increased revenue)')\nplt.ylabel('Net Gain')\nplt.title(f'Net Gain vs k for Customer {customer_id}')\nplt.show()\n\n# Total increased transaction revenue\ntotal_revenue_increase = sum(X_test['optimal_k'])\nprint(f\"Total increased transaction revenue with optimal k: {total_revenue_increase}\")\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Now plot the histogram\nimport seaborn as sns\nsns.histplot(X_test['optimal_k'], bins=50, color='blue', alpha=0.7)\nplt.title('Distribution of Optimal k values')\nplt.xlabel('k value')\nplt.show()\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_test['totals.transactionRevenue']","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"barWidth = 0.35\nr1 = np.arange(len(X_test['totals.transactionRevenue']))\nr2 = [x + barWidth for x in r1]\n# Create the bars\nX_test['increased.transactionRevenue'] = X_test['totals.transactionRevenue'] + X_test['optimal_k']\nplt.bar(r1, X_test['totals.transactionRevenue'], width=barWidth, color='gray', edgecolor='grey', label='Original Revenue')\nplt.bar(r2, X_test['increased.transactionRevenue'], width=barWidth, color='red', edgecolor='grey', label='Increased Revenue')\n\n# Title & subtitle\nplt.title('Comparison of Original and Increased Revenues', fontweight='bold')\nplt.xlabel('Data Points', fontweight='bold')\nplt.ylabel('Revenue', fontweight='bold')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport numpy as np\n\n# Sample data\nX_test['totals.transactionRevenue']\nincreased_revenue = X_test['optimal_k']\n\n# Set the positions and width for the bars\nbarWidth = 0.35\nr1 = np.arange(len(X_test['totals.transcationRevenue']))\nr2 = [x + barWidth for x in r1]\n\n# Create the bars\nplt.bar(r1, original_revenue, width=barWidth, color='blue', edgecolor='grey', label='Original Revenue')\nplt.bar(r2, increased_revenue, width=barWidth, color='red', edgecolor='grey', label='Increased Revenue')\n\n# Title & subtitle\nplt.title('Comparison of Original and Increased Revenues', fontweight='bold')\nplt.xlabel('Data Points', fontweight='bold')\nplt.ylabel('Revenue', fontweight='bold')\n\n# Add xticks on the middle of the group bars\nplt.xticks([r + barWidth for r in range(len(original_revenue))])\n\n# Create legend & display plot\nplt.legend()\nplt.show()\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}