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-Schange the notebook format
2022-11-30 16:06:03 -08:00
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"source": [
"# Section of the Notebook"
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"source": [
"1) **Choice of Metrics** \n",
"2) **Library import & Dataset** \n",
"3.1) **Feature generation** \n",
"3.2) **Data cleaning** \n",
"3.3) **Data scaling (Normalization | Standardization)** \n",
"4) **Dimensionality reduction** \n",
"5) **Clustering** \n",
"6) **Business Recommendations** "
]
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"source": [
"## Selection of Metrics"
]
},
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"source": [
"There are various metrics for clustering, for example:\n",
"* Silhouette coefficient \n",
"* Calinski-Harabasz Index \n",
"* Davies-Bouldin Index"
]
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"cell_type": "markdown",
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"tags": []
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"source": [
"## Importing Libraries and Dataset"
]
},
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"execution_count": 5,
"id": "36d8e078",
"metadata": {},
"outputs": [
{
"name": "stdout",
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"text": [
"^C\n"
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"source": [
"!conda install seaborn"
]
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"source": [
"import numpy as np\n",
"import pandas as pd \n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import plotly.express as px\n",
"import sklearn\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"from sklearn.metrics import silhouette_score\n",
"from sklearn.metrics import silhouette_samples\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.cluster import KMeans, AgglomerativeClustering, SpectralClustering, DBSCAN\n",
"from datetime import datetime\n",
"import warnings\n",
"import sys\n",
"import os"
]
},
{
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"execution_count": 22,
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"source": [
"df = pd.read_csv('marketing_campaign.csv')"
]
},
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" ID Year_Birth Education Marital_Status Income Kidhome Teenhome \\\n",
"0 5524 1957 Graduation Single 58138.0 0 0 \n",
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"2 4141 1965 Graduation Together 71613.0 0 0 \n",
"3 6182 1984 Graduation Together 26646.0 1 0 \n",
"4 5324 1981 PhD Married 58293.0 1 0 \n",
".. ... ... ... ... ... ... ... \n",
"95 7516 1983 Graduation Married 30096.0 1 0 \n",
"96 7247 1960 Graduation Widow 47916.0 0 1 \n",
"97 11100 1972 Graduation Divorced 51813.0 1 1 \n",
"98 4646 1951 2n Cycle Married 78497.0 0 0 \n",
"99 3037 1983 PhD Married 50150.0 0 0 \n",
"\n",
" Dt_Customer Recency MntWines ... AcceptedCmp3 AcceptedCmp4 \\\n",
"0 4/9/2012 58 635 ... 0 0 \n",
"1 8/3/2014 38 11 ... 0 0 \n",
"2 21-08-2013 26 426 ... 0 0 \n",
"3 10/2/2014 26 11 ... 0 0 \n",
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".. ... ... ... ... ... ... \n",
"95 22-05-2014 30 5 ... 0 0 \n",
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"97 11/4/2013 37 51 ... 0 0 \n",
"98 1/12/2013 44 207 ... 0 0 \n",
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" AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Z_CostContact \\\n",
"0 0 0 0 0 3 \n",
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"3 0 0 0 0 3 \n",
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"source": [
"# Features:\n",
"______________________________________________________________________________________________________________\n",
"### People\n",
"\n",
"* ID: Customer's unique identifier \n",
"* Year_Birth: Customer's birth year \n",
"* Education: Customer's education level \n",
"* Marital_Status: Customer's marital status \n",
"* Income: Customer's yearly household income \n",
"* Kidhome: Number of children in customer's household \n",
"* Teenhome: Number of teenagers in customer's household \n",
"* Dt_Customer: Date of customer's enrollment with the company \n",
"* Recency: Number of days since customer's last purchase \n",
"* Complain: 1 if the customer complained in the last 2 years, 0 otherwise \n",
"\n",
"### Products\n",
"\n",
"* MntWines: Amount spent on wine in last 2 years\n",
"* MntFruits: Amount spent on fruits in last 2 years\n",
"* MntMeatProducts: Amount spent on meat in last 2 years\n",
"* MntFishProducts: Amount spent on fish in last 2 years\n",
"* MntSweetProducts: Amount spent on sweets in last 2 years\n",
"* MntGoldProds: Amount spent on gold in last 2 years\n",
"\n",
"### Promotion\n",
"\n",
"* NumDealsPurchases: Number of purchases made with a discount\n",
"* AcceptedCmp1: 1 if customer accepted the offer in the 1st campaign, 0 otherwise\n",
"* AcceptedCmp2: 1 if customer accepted the offer in the 2nd campaign, 0 otherwise\n",
"* AcceptedCmp3: 1 if customer accepted the offer in the 3rd campaign, 0 otherwise\n",
"* AcceptedCmp4: 1 if customer accepted the offer in the 4th campaign, 0 otherwise\n",
"* AcceptedCmp5: 1 if customer accepted the offer in the 5th campaign, 0 otherwise\n",
"* Response: 1 if customer accepted the offer in the last campaign, 0 otherwise\n",
"\n",
"### Place\n",
"\n",
"* NumWebPurchases: Number of purchases made through the company’s website\n",
"* NumCatalogPurchases: Number of purchases made using a catalogue\n",
"* NumStorePurchases: Number of purchases made directly in stores\n",
"* NumWebVisitsMonth: Number of visits to company’s website in the last month\n",
"______________________________________________________________________________________________________________\n"
]
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" # Column Non-Null Count Dtype \n",
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" 8 Recency 2240 non-null int64 \n",
" 9 MntWines 2240 non-null int64 \n",
" 10 MntFruits 2240 non-null int64 \n",
" 11 MntMeatProducts 2240 non-null int64 \n",
" 12 MntFishProducts 2240 non-null int64 \n",
" 13 MntSweetProducts 2240 non-null int64 \n",
" 14 MntGoldProds 2240 non-null int64 \n",
" 15 NumDealsPurchases 2240 non-null int64 \n",
" 16 NumWebPurchases 2240 non-null int64 \n",
" 17 NumCatalogPurchases 2240 non-null int64 \n",
" 18 NumStorePurchases 2240 non-null int64 \n",
" 19 NumWebVisitsMonth 2240 non-null int64 \n",
" 20 AcceptedCmp3 2240 non-null int64 \n",
" 21 AcceptedCmp4 2240 non-null int64 \n",
" 22 AcceptedCmp5 2240 non-null int64 \n",
" 23 AcceptedCmp1 2240 non-null int64 \n",
" 24 AcceptedCmp2 2240 non-null int64 \n",
" 25 Complain 2240 non-null int64 \n",
" 26 Z_CostContact 2240 non-null int64 \n",
" 27 Z_Revenue 2240 non-null int64 \n",
" 28 Response 2240 non-null int64 \n",
"dtypes: float64(1), int64(25), object(3)\n",
"memory usage: 507.6+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "f7586411",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:07.561859Z",
"iopub.status.busy": "2022-08-28T13:50:07.561390Z",
"iopub.status.idle": "2022-08-28T13:50:07.567743Z",
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"papermill": {
"duration": 0.03321,
"end_time": "2022-08-28T13:50:07.570611",
"exception": false,
"start_time": "2022-08-28T13:50:07.537401",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Size of Dataset: (2240, 29)\n"
]
}
],
"source": [
"print(f'Size of Dataset: {df.shape}')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "f264ddf4",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:07.617293Z",
"iopub.status.busy": "2022-08-28T13:50:07.616780Z",
"iopub.status.idle": "2022-08-28T13:50:07.621925Z",
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# warnings are ignored.\n",
"warnings.filterwarnings('ignore') "
]
},
{
"cell_type": "markdown",
"id": "c2c96568",
"metadata": {},
"source": [
"### Optimize the memory usage (optional)\n",
"\n",
"* The dataframe entirely can be heavy of memory as it conists of all the dataset along with datatypes that can be reduced to fit with approriate data types."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d9a8233c",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:07.672093Z",
"iopub.status.busy": "2022-08-28T13:50:07.671209Z",
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},
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"duration": 0.041657,
"end_time": "2022-08-28T13:50:07.688614",
"exception": false,
"start_time": "2022-08-28T13:50:07.646957",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"def reduce_mem_usage(df: pd.DataFrame) -> pd.DataFrame:\n",
" \"\"\"Iterate through all the columns of a dataframe and modify the data type\n",
" to reduce memory usage\"\"\"\n",
" \n",
" start_mem = df.memory_usage().sum() / 1024**2\n",
" print('Memory usage of DataFrame is {:.2f} MB'.format(start_mem))\n",
" \n",
" for col in df.columns:\n",
" col_type = df[col].dtype\n",
" \n",
" if col_type != object:\n",
" c_min = df[col].min()\n",
" c_max = df[col].max()\n",
" if str(col_type)[:3] == 'int':\n",
" if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
" df[col] = df[col].astype(np.int8)\n",
" elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
" df[col] = df[col].astype(np.int16)\n",
" elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
" df[col] = df[col].astype(np.int32)\n",
" elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
" df[col] = df[col].astype(np.int64) \n",
" else:\n",
" if c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
" df[col] = df[col].astype(np.float32)\n",
" else:\n",
" df[col] = df[col].astype(np.float64)\n",
" else:\n",
" df[col] = df[col].astype('category')\n",
"\n",
" end_mem = df.memory_usage().sum() / 1024**2\n",
" print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n",
" print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n",
" \n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "dddb9771",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:07.736125Z",
"iopub.status.busy": "2022-08-28T13:50:07.735629Z",
"iopub.status.idle": "2022-08-28T13:50:07.767871Z",
"shell.execute_reply": "2022-08-28T13:50:07.765951Z"
},
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"duration": 0.059641,
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"start_time": "2022-08-28T13:50:07.711006",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Memory usage of DataFrame is 0.50 MB\n",
"Memory usage after optimization is: 0.11 MB\n",
"Decreased by 78.0%\n"
]
}
],
"source": [
"df = reduce_mem_usage(df)"
]
},
{
"cell_type": "markdown",
"id": "d8c840c5",
"metadata": {
"papermill": {
"duration": 0.022941,
"end_time": "2022-08-28T13:50:07.816120",
"exception": false,
"start_time": "2022-08-28T13:50:07.793179",
"status": "completed"
},
"tags": []
},
"source": [
"## 3.1) Feature generation"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "66231ffd",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:07.866854Z",
"iopub.status.busy": "2022-08-28T13:50:07.865910Z",
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"start_time": "2022-08-28T13:50:07.840297",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# Age of the Customer\n",
"df['Age'] = datetime.now().year - df['Year_Birth'] "
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "6d70dd7b",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:07.927656Z",
"iopub.status.busy": "2022-08-28T13:50:07.927187Z",
"iopub.status.idle": "2022-08-28T13:50:07.955447Z",
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},
"papermill": {
"duration": 0.056185,
"end_time": "2022-08-28T13:50:07.958468",
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"start_time": "2022-08-28T13:50:07.902283",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"ename": "ValueError",
"evalue": "time data '4/9/2012' does not match format '%d-%m-%Y' (match)",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"File \u001b[1;32mC:\\ProgramData\\Miniconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:510\u001b[0m, in \u001b[0;36m_to_datetime_with_format\u001b[1;34m(arg, orig_arg, name, tz, fmt, exact, errors, infer_datetime_format)\u001b[0m\n\u001b[0;32m 509\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 510\u001b[0m values, tz \u001b[38;5;241m=\u001b[39m \u001b[43mconversion\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdatetime_to_datetime64\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 511\u001b[0m dta \u001b[38;5;241m=\u001b[39m DatetimeArray(values, dtype\u001b[38;5;241m=\u001b[39mtz_to_dtype(tz))\n",
"File \u001b[1;32mC:\\ProgramData\\Miniconda3\\lib\\site-packages\\pandas\\_libs\\tslibs\\conversion.pyx:360\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.conversion.datetime_to_datetime64\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: Unrecognized value type: <class 'str'>",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"Input \u001b[1;32mIn [38]\u001b[0m, in \u001b[0;36m<cell line: 6>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m current_year \u001b[38;5;241m=\u001b[39m datetime\u001b[38;5;241m.\u001b[39mnow()\u001b[38;5;241m.\u001b[39myear\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m current_year \u001b[38;5;241m-\u001b[39m registration_year\n\u001b[1;32m----> 6\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYears_Since_Registration\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mget_different_year\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"Input \u001b[1;32mIn [38]\u001b[0m, in \u001b[0;36mget_different_year\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_different_year\u001b[39m():\n\u001b[1;32m----> 2\u001b[0m registration_year \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mDt_Customer\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m%d\u001b[39;49;00m\u001b[38;5;124;43m-\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mm-\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mY\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x: x\u001b[38;5;241m.\u001b[39myear)\n\u001b[0;32m 3\u001b[0m current_year \u001b[38;5;241m=\u001b[39m datetime\u001b[38;5;241m.\u001b[39mnow()\u001b[38;5;241m.\u001b[39myear\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m current_year \u001b[38;5;241m-\u001b[39m registration_year\n",
"File \u001b[1;32mC:\\ProgramData\\Miniconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1051\u001b[0m, in \u001b[0;36mto_datetime\u001b[1;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[0;32m 1049\u001b[0m result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mmap(cache_array)\n\u001b[0;32m 1050\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1051\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[43mconvert_listlike\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1052\u001b[0m result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39m_constructor(values, index\u001b[38;5;241m=\u001b[39marg\u001b[38;5;241m.\u001b[39mindex, name\u001b[38;5;241m=\u001b[39marg\u001b[38;5;241m.\u001b[39mname)\n\u001b[0;32m 1053\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, (ABCDataFrame, abc\u001b[38;5;241m.\u001b[39mMutableMapping)):\n",
"File \u001b[1;32mC:\\ProgramData\\Miniconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:394\u001b[0m, in \u001b[0;36m_convert_listlike_datetimes\u001b[1;34m(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[0;32m 391\u001b[0m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 393\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 394\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43m_to_datetime_with_format\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 395\u001b[0m \u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morig_arg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtz\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexact\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minfer_datetime_format\u001b[49m\n\u001b[0;32m 396\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 397\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m res \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 398\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\n",
"File \u001b[1;32mC:\\ProgramData\\Miniconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:514\u001b[0m, in \u001b[0;36m_to_datetime_with_format\u001b[1;34m(arg, orig_arg, name, tz, fmt, exact, errors, infer_datetime_format)\u001b[0m\n\u001b[0;32m 512\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m DatetimeIndex\u001b[38;5;241m.\u001b[39m_simple_new(dta, name\u001b[38;5;241m=\u001b[39mname)\n\u001b[0;32m 513\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[1;32m--> 514\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m err\n",
"File \u001b[1;32mC:\\ProgramData\\Miniconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:501\u001b[0m, in \u001b[0;36m_to_datetime_with_format\u001b[1;34m(arg, orig_arg, name, tz, fmt, exact, errors, infer_datetime_format)\u001b[0m\n\u001b[0;32m 498\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _box_as_indexlike(result, utc\u001b[38;5;241m=\u001b[39mutc, name\u001b[38;5;241m=\u001b[39mname)\n\u001b[0;32m 500\u001b[0m \u001b[38;5;66;03m# fallback\u001b[39;00m\n\u001b[1;32m--> 501\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43m_array_strptime_with_fallback\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 502\u001b[0m \u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtz\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfmt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexact\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minfer_datetime_format\u001b[49m\n\u001b[0;32m 503\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 504\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\n\u001b[0;32m 506\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m 507\u001b[0m \u001b[38;5;66;03m# Fallback to try to convert datetime objects if timezone-aware\u001b[39;00m\n\u001b[0;32m 508\u001b[0m \u001b[38;5;66;03m# datetime objects are found without passing `utc=True`\u001b[39;00m\n",
"File \u001b[1;32mC:\\ProgramData\\Miniconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:437\u001b[0m, in \u001b[0;36m_array_strptime_with_fallback\u001b[1;34m(arg, name, tz, fmt, exact, errors, infer_datetime_format)\u001b[0m\n\u001b[0;32m 434\u001b[0m utc \u001b[38;5;241m=\u001b[39m tz \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutc\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 436\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 437\u001b[0m result, timezones \u001b[38;5;241m=\u001b[39m \u001b[43marray_strptime\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfmt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexact\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexact\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 438\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mZ\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m fmt \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mz\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m fmt:\n\u001b[0;32m 439\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _return_parsed_timezone_results(result, timezones, tz, name)\n",
"File \u001b[1;32mC:\\ProgramData\\Miniconda3\\lib\\site-packages\\pandas\\_libs\\tslibs\\strptime.pyx:150\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: time data '4/9/2012' does not match format '%d-%m-%Y' (match)"
]
}
],
"source": [
"def get_different_year():\n",
" registration_year = pd.to_datetime(df['Dt_Customer'], format='%d-%m-%Y').apply(lambda x: x.year)\n",
" current_year = datetime.now().year\n",
" return current_year - registration_year\n",
"\n",
"df['Years_Since_Registration'] = get_different_year()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "1add1b3f",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:08.005426Z",
"iopub.status.busy": "2022-08-28T13:50:08.004545Z",
"iopub.status.idle": "2022-08-28T13:50:08.016059Z",
"shell.execute_reply": "2022-08-28T13:50:08.015005Z"
},
"papermill": {
"duration": 0.037797,
"end_time": "2022-08-28T13:50:08.018592",
"exception": false,
"start_time": "2022-08-28T13:50:07.980795",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"df[\"Education\"] = df[\"Education\"].replace({\"Basic\": 0, \"Graduation\": 1, \"2n Cycle\": 2, \"Master\": 2, \"PhD\": 3})\n",
"# Encoded in numbers in order"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "1e356ea5",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:08.066519Z",
"iopub.status.busy": "2022-08-28T13:50:08.065713Z",
"iopub.status.idle": "2022-08-28T13:50:08.081091Z",
"shell.execute_reply": "2022-08-28T13:50:08.079966Z"
},
"papermill": {
"duration": 0.043231,
"end_time": "2022-08-28T13:50:08.083904",
"exception": false,
"start_time": "2022-08-28T13:50:08.040673",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"df['Marital_Status'] = df[\"Marital_Status\"].replace({\"Married\":\"Partner\", \"Together\":\"Partner\", \"Absurd\":\"Alone\",\n",
" \"Widow\":\"Alone\", \"YOLO\":\"Alone\", \"Divorced\":\"Alone\", \"Single\":\"Alone\"})\n",
"\n",
"step_2 = df['Marital_Status'].replace({'Alone': 1, 'Partner': 2})\n",
"\n",
"df[\"Family_Size\"] = step_2 + df['Kidhome'] + df['Teenhome'] # Total number of people in the family\n",
"\n",
"df = pd.concat([df, pd.get_dummies(df['Marital_Status'])], axis=1) # Separate features for Alone and Partner"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "ac685e10",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:08.131079Z",
"iopub.status.busy": "2022-08-28T13:50:08.130324Z",
"iopub.status.idle": "2022-08-28T13:50:08.139672Z",
"shell.execute_reply": "2022-08-28T13:50:08.138374Z"
},
"papermill": {
"duration": 0.036065,
"end_time": "2022-08-28T13:50:08.142371",
"exception": false,
"start_time": "2022-08-28T13:50:08.106306",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"mnt_cols = ['MntWines', 'MntFruits', 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts', 'MntGoldProds']\n",
"df['Sum_Mnt'] = df[mnt_cols].sum(axis=1) # Total amount spent on products"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "bf8e3514",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:08.190191Z",
"iopub.status.busy": "2022-08-28T13:50:08.189415Z",
"iopub.status.idle": "2022-08-28T13:50:08.198065Z",
"shell.execute_reply": "2022-08-28T13:50:08.197120Z"
},
"papermill": {
"duration": 0.035303,
"end_time": "2022-08-28T13:50:08.200563",
"exception": false,
"start_time": "2022-08-28T13:50:08.165260",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"accepted_cmp_cols = ['AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5',\n",
" 'Response']\n",
"df['Num_Accepted_Cmp'] = df[accepted_cmp_cols].sum(axis=1) \n",
"# Number of companies in which the client accepted the offer"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "bdf8dac4",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:08.247275Z",
"iopub.status.busy": "2022-08-28T13:50:08.246531Z",
"iopub.status.idle": "2022-08-28T13:50:08.255458Z",
"shell.execute_reply": "2022-08-28T13:50:08.254344Z"
},
"papermill": {
"duration": 0.035527,
"end_time": "2022-08-28T13:50:08.258183",
"exception": false,
"start_time": "2022-08-28T13:50:08.222656",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"total_purchases = ['NumWebPurchases', 'NumCatalogPurchases', 'NumStorePurchases']\n",
"df['Num_Total_Purchases'] = df[total_purchases].sum(axis=1) # Total number of purchases"
]
},
{
"cell_type": "markdown",
"id": "d8ef8896",
"metadata": {
"papermill": {
"duration": 0.0219,
"end_time": "2022-08-28T13:50:08.302246",
"exception": false,
"start_time": "2022-08-28T13:50:08.280346",
"status": "completed"
},
"tags": []
},
"source": [
"# Let's look at EDA:\n",
"______________________________________________________________________________________________________________"
]
},
{
"cell_type": "markdown",
"id": "40e210cd",
"metadata": {
"papermill": {
"duration": 0.021869,
"end_time": "2022-08-28T13:50:08.346271",
"exception": false,
"start_time": "2022-08-28T13:50:08.324402",
"status": "completed"
},
"tags": []
},
"source": [
"## First, let's look at the correlation matrix:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "94fe9d5c",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:08.393693Z",
"iopub.status.busy": "2022-08-28T13:50:08.392815Z",
"iopub.status.idle": "2022-08-28T13:50:10.957802Z",
"shell.execute_reply": "2022-08-28T13:50:10.956231Z"
},
"papermill": {
"duration": 2.594148,
"end_time": "2022-08-28T13:50:10.962906",
"exception": false,
"start_time": "2022-08-28T13:50:08.368758",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1800x1008 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"to_corr = ['Age', 'Income', 'Kidhome', 'Teenhome', 'Recency', 'Complain', \n",
" 'MntWines', 'MntFruits', 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts', \n",
" 'MntGoldProds', 'NumDealsPurchases', 'AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', \n",
" 'AcceptedCmp4', 'AcceptedCmp5', 'Response', 'NumWebPurchases', 'NumCatalogPurchases', \n",
" 'NumStorePurchases', 'NumWebVisitsMonth', 'Years_Since_Registration', 'Family_Size',\n",
" 'Sum_Mnt', 'Num_Accepted_Cmp', 'Num_Total_Purchases', 'Alone', 'Partner']\n",
"\n",
"cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
"matrix = np.triu(df[to_corr].corr())\n",
"plt.figure(figsize=(25, 14))\n",
"plt.title('Correlation matrix', fontsize=18)\n",
"sns.heatmap(df[to_corr].corr(), annot=True,\n",
" fmt='.1f', vmin=-0.4, center=0, cmap=cmap, mask=matrix)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "90faf051",
"metadata": {
"papermill": {
"duration": 0.026543,
"end_time": "2022-08-28T13:50:11.019982",
"exception": false,
"start_time": "2022-08-28T13:50:10.993439",
"status": "completed"
},
"tags": []
},
"source": [
"## Next, look at the distributions of quantitative variables:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "2116d718",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:11.074918Z",
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"shell.execute_reply": "2022-08-28T13:50:23.867626Z"
},
"papermill": {
"duration": 12.833249,
"end_time": "2022-08-28T13:50:23.878991",
"exception": false,
"start_time": "2022-08-28T13:50:11.045742",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1159.38x1080 with 42 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"to_plot = ['Income', 'Recency', 'Age', 'Years_Since_Registration', 'Sum_Mnt',\n",
" 'Num_Total_Purchases', 'Marital_Status']\n",
"sns.pairplot(df[to_plot], hue='Marital_Status', palette='Set1')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "301760a7",
"metadata": {
"papermill": {
"duration": 0.037934,
"end_time": "2022-08-28T13:50:23.956788",
"exception": false,
"start_time": "2022-08-28T13:50:23.918854",
"status": "completed"
},
"tags": []
},
"source": [
"## 3.2) Data cleaning"
]
},
{
"cell_type": "markdown",
"id": "becb2250",
"metadata": {
"papermill": {
"duration": 0.0365,
"end_time": "2022-08-28T13:50:24.030576",
"exception": false,
"start_time": "2022-08-28T13:50:23.994076",
"status": "completed"
},
"tags": []
},
"source": [
"## Remove gaps in the data:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "e85427d0",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:24.107029Z",
"iopub.status.busy": "2022-08-28T13:50:24.106545Z",
"iopub.status.idle": "2022-08-28T13:50:24.119539Z",
"shell.execute_reply": "2022-08-28T13:50:24.118496Z"
},
"papermill": {
"duration": 0.053881,
"end_time": "2022-08-28T13:50:24.121797",
"exception": false,
"start_time": "2022-08-28T13:50:24.067916",
"status": "completed"
},
"scrolled": true,
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"ID 0\n",
"Year_Birth 0\n",
"Education 0\n",
"Marital_Status 0\n",
"Income 24\n",
"Kidhome 0\n",
"Teenhome 0\n",
"Dt_Customer 0\n",
"Recency 0\n",
"MntWines 0\n",
"MntFruits 0\n",
"MntMeatProducts 0\n",
"MntFishProducts 0\n",
"MntSweetProducts 0\n",
"MntGoldProds 0\n",
"NumDealsPurchases 0\n",
"NumWebPurchases 0\n",
"NumCatalogPurchases 0\n",
"NumStorePurchases 0\n",
"NumWebVisitsMonth 0\n",
"AcceptedCmp3 0\n",
"AcceptedCmp4 0\n",
"AcceptedCmp5 0\n",
"AcceptedCmp1 0\n",
"AcceptedCmp2 0\n",
"Complain 0\n",
"Z_CostContact 0\n",
"Z_Revenue 0\n",
"Response 0\n",
"Age 0\n",
"Years_Since_Registration 0\n",
"Family_Size 0\n",
"Alone 0\n",
"Partner 0\n",
"Sum_Mnt 0\n",
"Num_Accepted_Cmp 0\n",
"Num_Total_Purchases 0\n",
"dtype: int64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "5a689bb4",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:24.197184Z",
"iopub.status.busy": "2022-08-28T13:50:24.195980Z",
"iopub.status.idle": "2022-08-28T13:50:24.205508Z",
"shell.execute_reply": "2022-08-28T13:50:24.204269Z"
},
"papermill": {
"duration": 0.051032,
"end_time": "2022-08-28T13:50:24.208553",
"exception": false,
"start_time": "2022-08-28T13:50:24.157521",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"df.dropna(inplace=True) # Removed objects with gaps in income"
]
},
{
"cell_type": "markdown",
"id": "cc804158",
"metadata": {
"papermill": {
"duration": 0.036219,
"end_time": "2022-08-28T13:50:24.280612",
"exception": false,
"start_time": "2022-08-28T13:50:24.244393",
"status": "completed"
},
"tags": []
},
"source": [
"## Remove outliers in the data:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "21982d02",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:24.357434Z",
"iopub.status.busy": "2022-08-28T13:50:24.356548Z",
"iopub.status.idle": "2022-08-28T13:50:24.370733Z",
"shell.execute_reply": "2022-08-28T13:50:24.369666Z"
},
"papermill": {
"duration": 0.05638,
"end_time": "2022-08-28T13:50:24.373367",
"exception": false,
"start_time": "2022-08-28T13:50:24.316987",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"def remove_outliers(data: pd.DataFrame, column: str) -> pd.Series:\n",
" q3, q1 = np.nanpercentile(data[column], [75, 25])\n",
" iqr = q3 - q1\n",
" upper_bound = q3 + 1.5 * iqr\n",
" lower_bound = q1 - 1.5 * iqr\n",
" data = data[(data[column] > lower_bound) & (data[column] < upper_bound)]\n",
"\n",
" return data\n",
"\n",
"df = remove_outliers(df, 'Age')\n",
"df = remove_outliers(df, 'Income')"
]
},
{
"cell_type": "markdown",
"id": "bdddf64e",
"metadata": {
"papermill": {
"duration": 0.035761,
"end_time": "2022-08-28T13:50:24.445017",
"exception": false,
"start_time": "2022-08-28T13:50:24.409256",
"status": "completed"
},
"tags": []
},
"source": [
"## Remove unnecessary features:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "fe757220",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:24.518855Z",
"iopub.status.busy": "2022-08-28T13:50:24.518398Z",
"iopub.status.idle": "2022-08-28T13:50:24.526627Z",
"shell.execute_reply": "2022-08-28T13:50:24.525500Z"
},
"papermill": {
"duration": 0.048269,
"end_time": "2022-08-28T13:50:24.529075",
"exception": false,
"start_time": "2022-08-28T13:50:24.480806",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"df.drop(['Year_Birth', 'ID', 'Z_CostContact', 'Z_Revenue', 'Dt_Customer', 'Marital_Status'], axis=1, inplace=True)"
]
},
{
"cell_type": "markdown",
"id": "2a8eda0c",
"metadata": {
"papermill": {
"duration": 0.03541,
"end_time": "2022-08-28T13:50:24.600872",
"exception": false,
"start_time": "2022-08-28T13:50:24.565462",
"status": "completed"
},
"tags": []
},
"source": [
"## 3.3) Data scaling (Normalization | Standardization)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "95d6b516",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:24.748441Z",
"iopub.status.busy": "2022-08-28T13:50:24.747424Z",
"iopub.status.idle": "2022-08-28T13:50:24.760373Z",
"shell.execute_reply": "2022-08-28T13:50:24.759307Z"
},
"papermill": {
"duration": 0.055109,
"end_time": "2022-08-28T13:50:24.763522",
"exception": false,
"start_time": "2022-08-28T13:50:24.708413",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"ename": "ValueError",
"evalue": "could not convert string to float: 'Graduation'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"Input \u001b[1;32mIn [40]\u001b[0m, in \u001b[0;36m<cell line: 6>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m mms \u001b[38;5;241m=\u001b[39m MinMaxScaler()\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pd\u001b[38;5;241m.\u001b[39mDataFrame(data\u001b[38;5;241m=\u001b[39mmms\u001b[38;5;241m.\u001b[39mfit_transform(df), columns\u001b[38;5;241m=\u001b[39mdf\u001b[38;5;241m.\u001b[39mcolumns)\n\u001b[1;32m----> 6\u001b[0m df_scaled \u001b[38;5;241m=\u001b[39m \u001b[43mscaling_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;66;03m# to ensure\u001b[39;00m\n\u001b[0;32m 8\u001b[0m df_scaled\u001b[38;5;241m.\u001b[39mindex \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mindex\n",
"Input \u001b[1;32mIn [40]\u001b[0m, in \u001b[0;36mscaling_func\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mscaling_func\u001b[39m(df: pd\u001b[38;5;241m.\u001b[39mDataFrame) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 2\u001b[0m mms \u001b[38;5;241m=\u001b[39m MinMaxScaler()\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pd\u001b[38;5;241m.\u001b[39mDataFrame(data\u001b[38;5;241m=\u001b[39m\u001b[43mmms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m, columns\u001b[38;5;241m=\u001b[39mdf\u001b[38;5;241m.\u001b[39mcolumns)\n",
"File \u001b[1;32mC:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\base.py:867\u001b[0m, in \u001b[0;36mTransformerMixin.fit_transform\u001b[1;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[0;32m 863\u001b[0m \u001b[38;5;66;03m# non-optimized default implementation; override when a better\u001b[39;00m\n\u001b[0;32m 864\u001b[0m \u001b[38;5;66;03m# method is possible for a given clustering algorithm\u001b[39;00m\n\u001b[0;32m 865\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 866\u001b[0m \u001b[38;5;66;03m# fit method of arity 1 (unsupervised transformation)\u001b[39;00m\n\u001b[1;32m--> 867\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit(X, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\u001b[38;5;241m.\u001b[39mtransform(X)\n\u001b[0;32m 868\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 869\u001b[0m \u001b[38;5;66;03m# fit method of arity 2 (supervised transformation)\u001b[39;00m\n\u001b[0;32m 870\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit(X, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\u001b[38;5;241m.\u001b[39mtransform(X)\n",
"File \u001b[1;32mC:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:420\u001b[0m, in \u001b[0;36mMinMaxScaler.fit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m 418\u001b[0m \u001b[38;5;66;03m# Reset internal state before fitting\u001b[39;00m\n\u001b[0;32m 419\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()\n\u001b[1;32m--> 420\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpartial_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32mC:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:457\u001b[0m, in \u001b[0;36mMinMaxScaler.partial_fit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m 451\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m 452\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMinMaxScaler does not support sparse input. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 453\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConsider using MaxAbsScaler instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 454\u001b[0m )\n\u001b[0;32m 456\u001b[0m first_pass \u001b[38;5;241m=\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_samples_seen_\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 457\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 458\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 459\u001b[0m \u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfirst_pass\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 460\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mFLOAT_DTYPES\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 461\u001b[0m \u001b[43m \u001b[49m\u001b[43mforce_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mallow-nan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 462\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 464\u001b[0m data_min \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mnanmin(X, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m 465\u001b[0m data_max \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mnanmax(X, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n",
"File \u001b[1;32mC:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\base.py:577\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m 575\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValidation should be done on X, y or both.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 576\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m no_val_y:\n\u001b[1;32m--> 577\u001b[0m X \u001b[38;5;241m=\u001b[39m check_array(X, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n\u001b[0;32m 578\u001b[0m out \u001b[38;5;241m=\u001b[39m X\n\u001b[0;32m 579\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_y:\n",
"File \u001b[1;32mC:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:856\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m 854\u001b[0m array \u001b[38;5;241m=\u001b[39m array\u001b[38;5;241m.\u001b[39mastype(dtype, casting\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munsafe\u001b[39m\u001b[38;5;124m\"\u001b[39m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m 855\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 856\u001b[0m array \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 857\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ComplexWarning \u001b[38;5;28;01mas\u001b[39;00m complex_warning:\n\u001b[0;32m 858\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 859\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComplex data not supported\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[0;32m 860\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcomplex_warning\u001b[39;00m\n",
"File \u001b[1;32mC:\\ProgramData\\Miniconda3\\lib\\site-packages\\pandas\\core\\generic.py:2064\u001b[0m, in \u001b[0;36mNDFrame.__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m 2063\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array__\u001b[39m(\u001b[38;5;28mself\u001b[39m, dtype: npt\u001b[38;5;241m.\u001b[39mDTypeLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[1;32m-> 2064\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Graduation'"
]
}
],
"source": [
"def scaling_func(df: pd.DataFrame) -> None:\n",
" mms = MinMaxScaler()\n",
" return pd.DataFrame(data=mms.fit_transform(df), columns=df.columns)\n",
"\n",
"\n",
"df_scaled = scaling_func(df)\n",
"# to ensure\n",
"df_scaled.index = df.index "
]
},
{
"cell_type": "markdown",
"id": "238ce006",
"metadata": {
"papermill": {
"duration": 0.036003,
"end_time": "2022-08-28T13:50:24.835891",
"exception": false,
"start_time": "2022-08-28T13:50:24.799888",
"status": "completed"
},
"tags": []
},
"source": [
"## 4) Dimensionality reduction"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "6c8a817f",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:24.986241Z",
"iopub.status.busy": "2022-08-28T13:50:24.985289Z",
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"shell.execute_reply": "2022-08-28T13:50:25.126448Z"
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df_scaled' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"Input \u001b[1;32mIn [41]\u001b[0m, in \u001b[0;36m<cell line: 7>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n\u001b[0;32m 6\u001b[0m \u001b[38;5;66;03m# df: scaling + dimensionality reduction\u001b[39;00m\n\u001b[1;32m----> 7\u001b[0m df_transform \u001b[38;5;241m=\u001b[39m dim_reduction(\u001b[43mdf_scaled\u001b[49m) \n\u001b[0;32m 8\u001b[0m df_transform\u001b[38;5;241m.\u001b[39mindex \u001b[38;5;241m=\u001b[39m df_scaled\u001b[38;5;241m.\u001b[39mindex\n",
"\u001b[1;31mNameError\u001b[0m: name 'df_scaled' is not defined"
]
}
],
"source": [
"def dim_reduction(data: pd.DataFrame) -> pd.DataFrame:\n",
" n_components = 8\n",
" pca = PCA(n_components=n_components, random_state=42)\n",
" data = pd.DataFrame(pca.fit_transform(data), columns =([f'PC{i}' for i in range(1, n_components + 1)]))\n",
" return data\n",
"\n",
"# df: scaling + dimensionality reduction\n",
"df_transform = dim_reduction(df_scaled) \n",
"df_transform.index = df_scaled.index "
]
},
{
"cell_type": "markdown",
"id": "811f7208",
"metadata": {
"papermill": {
"duration": 0.036946,
"end_time": "2022-08-28T13:50:25.291626",
"exception": false,
"start_time": "2022-08-28T13:50:25.254680",
"status": "completed"
},
"tags": []
},
"source": [
"<div style=\"padding:25px;color:white;margin:0;font-size:250%;text-align:center;display:fill;border-radius:10px;background-color:#298f50;overflow:hidden;font-weight:500;font-family:magra\">5) Clustering</div>"
]
},
{
"cell_type": "markdown",
"id": "35bdc8bd",
"metadata": {
"papermill": {
"duration": 0.03694,
"end_time": "2022-08-28T13:50:25.439167",
"exception": false,
"start_time": "2022-08-28T13:50:25.402227",
"status": "completed"
},
"tags": []
},
"source": [
"In this work, the following clustering algorithms will be used: (https://scikit-learn.org/stable/modules/clustering.html)\n",
"* K-means ++ \n",
"* Aglomerative clustering\n",
"* Spectral clustering\n",
"\n",
"\n",
"It is noteworthy that in these algorithms it is necessary to pre-specify the number of clusters. \n",
"We assume that the minimum number of clusters is two, the maximum number of clusters is eight. Based on this, for different cases of the number of clusters, we will build models. \n",
"If the metric is maximum for models with a large number of clusters, then we will iteratively increase the maximum number of clusters and build new models.\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "7f355417",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:50:25.515926Z",
"iopub.status.busy": "2022-08-28T13:50:25.515437Z",
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"shell.execute_reply": "2022-08-28T13:52:29.396253Z"
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"exception": false,
"start_time": "2022-08-28T13:50:25.476107",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABasAAAFWCAYAAABnxMEoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAByTElEQVR4nO3dd3gU1dvG8XuTECC0BAi9l4SWEEApAgIBBCkqKEgRBBQQsSAgiEpQVFBAEYMiVQQBidKkCEhXUUB676GXUEJJgLR9/+Dd/SWkkITNTnbz/VyXl2R2dubZ2ZPNmXvPnDGZzWazAAAAAAAAAAAwkIvRBQAAAAAAAAAAQFgNAAAAAAAAADAcYTUAAAAAAAAAwHCE1QAAAAAAAAAAwxFWAwAAAAAAAAAMR1gNAAAAAAAAADAcYTXgoIKDg+Xr6ytfX1+jS8lwZ8+etb7WhQsX2mWfd+/eVaNGjVS5cmWdPHkyTc8NDAyUr6+v3nvvvQyqLnnvvfeefH19FRgY+NB1t2zZYj2uW7ZssUN1eFBMTIwCAwNVuXJlHTp0yOhyAABZXLdu3eTr66tu3bpZl1n6CsHBwZLoP6SHPfvtS5cuVYsWLVStWjX5+vpq5syZGb7P+B7sByfXj9+3b586deqkgIAA+fr66vXXX5cknTlzRq+88opq1aolX19fPfvss3at3whZ6bzO4tChQ6pcubICAwMVExOT6ucZcV4YX1rO87Li+5rZnDhxQpUrV1ajRo107949o8tBGhBWA5lQVFSUZs6cqRdffFG1atWSv7+/nnrqKb3//vs6fvx4hu/f6E7Ag9zd3VW9enVVr15d+fPnt8s+Z8+erYsXL6pJkyYqW7asXfZpCyVLllT16tVVuXJl67K0BNhGsrS5+J2/Q4cOqV69evL19VXLli116dKlBCfJvr6+Wrp0aYLtTJo0KcHjZ8+etfdLSTU3Nzd1795dcXFx+vLLL40uBwDgpKKjozVlyhS1atVKAQEBqlmzppo1a6Z+/fppz5491vXKly+v6tWrq3z58gZW+2iSC5McNWRPSzh29epVDRs2TKGhocqTJ4+qV6+uQoUK2aHK5CXXj//ggw+0c+dOmc1m+fn5Wfvbo0eP1l9//aW7d+9aA/fM6MEvcTKrzHZeZ/Hll18qLi5O3bt3l5ubm9HlpFrlypVVvXp1lSxZ0rrMyIFKqRX/8y9+O1iyZImqVKkiX19fvfnmm4qOjk4QslepUkXnz59PsK2ePXtaH8/s55flypVTkyZNdPHiRc2aNcvocpAGjvOpAGQRN27cUI8ePXTgwAFJkoeHh0qXLq2LFy9qwYIF8vHxcdgTiKioKLm7u6f5eYUKFVJISEgGVJS02NhYzZkzR5IcZjSH2WxWbGys+vfvr/79+xtdjk3s3btXr776qsLDw+Xj46OZM2eqQIECCg0NTbDe7Nmz1bZtW0n3RyrPmzfPgGrTr02bNvriiy+0adMmhYaGqkyZMkaXBABwMmPHjtWPP/4oSSpdurSyZ8+u8+fPa926dWrevLn8/f0lSR999JGBVeJRhYaGKjo6WtL997xBgwaPvM309t8tkuvHHzt2TJL08ssva+DAgYmWt2zZ0iZf5MfExMjV1VUmk+mRt4X/eZR2cfLkSW3atEmurq7WPnxmZ3m93377rdGl2ExISIhGjBihuLg46/nIg18cxMbGau7cuRo8eLAk6fjx49q8ebMR5abbM888o7Vr12rOnDl65ZVX5OLCmF1HwLsEZDIjR460BtW9evXS1q1btXTpUm3btk3z589XpUqVkn1uUpduJnX50Z49e9SzZ0/VqVNH1apVU6NGjdSnTx/t3btXCxcuVNOmTa3rDhs2LNE2ly9fro4dOyogIEABAQHq3r27tm/fbn08/je3ISEh6tatm/z8/DR37twk675y5YreffddNWjQQNWqVVO9evXUpUsXLVmyRFLSIwIso4WT+s8iLCxMH3zwgRo2bKhq1aqpSZMmGjdunKKiolJ8D7Zu3aoLFy7Izc1NTz75pHX5uXPn9Oqrr6pRo0by9/eXv7+/2rRpo5kzZ8psNqe4zaNHj6pLly7y8/NTy5Yt9ccffyT5LXx4eLhGjhypxo0bq2rVqqpXr54GDhyo06dPW9eJ/55u3LhRrVq1UtWqVXX06NFEo6gDAwO1aNEia/3JjSi6dOmS+vfvr4CAAAUGBuqXX36xPhb//fz111/VrVs3+fv7q0OHDjp+/Lj+/vtvtWnTRjVq1FDv3r0VFhaW5HPTMopp+/bt6tGjh8LDw1W1alXNmjVLBQoUSLRetmzZtHv3buuosNWrV+vSpUvKli1bktvds2eP+vbtq9q1a6tatWpq27atFixYkGCdL774Qq1bt9Zjjz2mqlWrqkGDBho6dKguX76c5Hvw77//ql27dvL391e7du20a9cu63oPa9uSVLBgQWtIEH85AAC2snz5cknS66+/rtWrV2vp0qX677//NH/+fOvfICnpvmRKUuo/SNL58+c1ZMgQ1a9fX1WrVlXDhg01fPhwXb161bpOUleALVy4MMkrpP766y91795dNWvWlJ+fnzp06KB169ZJ+l9/8dy5c5KkRYsWJRj92r17d+t2unfvnqAPZjabNWfOHD3zzDPy9/dXrVq19Nprr1mD0+TE7w/8888/evbZZ+Xn56dnnnlG//3330OP34IFC9S+fXv5+/srICBAHTt21O+///7Q15NcLV26dLH+/MorryTof/3333/W6TWqVaumFi1aaNKkSdZwW/rfCNF3331Xn3/+uerUqaOOHTsmW//Ro0fVuXNna/92zZo1idZ5sB9v6Rtapn6YPHmy9b3w9fXVqVOnJEnLli1L8B7dvn1bo0ePVmBgoKpVq6YGDRooKChIN2/etO4rfltauHChdd1bt25JSts5zMKFC9W3b19Vr149Qdu2rGMxceLEVI0wXbFihTp37qwaNWqoevXqatOmTZLHy8IRzusiIyP18ccfq3HjxvLz81OdOnXUoUMH/fDDDykeC0t/19/fP0H/ftOmTerSpYvq1aunatWqqWbNmurSpYs2btyY4vYkac2aNWrRooX8/PzUtWtXbdy4McmRxEeOHNEbb7xhPV6BgYH64osvFBERkeSxnzJliho0aKD69etLSjiKOi2/ozt27NDzzz+v6tWrJzpfiP++/vXXX3r66adVvXp1vf3227p9+7ZmzpypBg0aqE6dOvrkk08STJuS3qlGZs+eraCgIMXFxal9+/YaO3ZskiPcs2XLpl9++UV3796VJOvo5OTOtR7WniIjI/X6668rMDBQAQEBqlatmp566ilNmDAhwTl6/Pdgzpw5CgwMVI0aNdS3b98E55optX+LJ598Uq6urrpw4YJDXVWT1RFWA5nIrVu3tHLlSklSpUqVNGTIkAR/CAICAlS3bt1H2kdcXJz69OmjzZs3y9XVVRUrVlRMTIw2btyoEydOKH/+/AmmkLBMK2EZzT1jxgwNHDhQu3fvlre3tzw9PbVlyxa9/PLL2rlzZ6L9jRw5UkePHlXJkiWT/Rbz448/1m+//aaIiAj5+PgoZ86c2rlzp7Zu3Zrs67DUZfnPcpyyZ88uSbp+/bpefPFF/frrr7p9+7bKlSunsLAwTZ06VW+99VaKx8jyR6x8+fLKmTOndfm1a9f0559/Wh/LnTu3jh49qtGjRycbxEvSvXv31Lt3b23fvl1xcXFycXHR4MGDE/yhtaxn+YN8+fJllSlTRhEREdY/+hcvXky07f79++vevXvJXuJZuXJleXl5SbrfqbAcr9y5cydYb/jw4Tp48KBcXV117tw5BQUFJTnlzMcff6zLly8rLi7OGvz2799fsbGxunPnjjZt2qTPP/882WORGvv379err76q27dvKyAgQD/++KP1NTyoRYsWku53uOL/37I8vh07dqhLly7asGGD3NzcVLp0aR05ckTvv/++ZsyYYV3vzz//1KVLl1S0aFGVKlVKV65c0eLFi61zKT6od+/eunPnjmJjY3XgwAENHDjQ2olMbdv28/OTJDpQAIAMERcXJ0navHmz1q1bp7CwMJl
"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1800x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"****************************************************************************************************************\n"
]
}
],
"source": [
"class Clustering:\n",
" algorithms = ['KMeans', 'AgglomerativeClustering', 'SpectralClustering']\n",
" min_n_clusters = 2\n",
" max_n_clusters = 8\n",
" # Model distortions for different hyperparameters(n_clusters) \n",
" kmeans_distortions = [] \n",
" \n",
" @staticmethod\n",
" def sse_plot(): \n",
" \"\"\"Graphs for the SSE values (KMeans)\"\"\"\n",
" plt.figure(figsize=(25, 8))\n",
" sns.set_style(\"darkgrid\")\n",
" plt.title('Distortion values for different number of clusters (for Kmeans)', fontsize=14, fontweight='bold')\n",
" plt.xlabel('Clusters', fontsize=14, fontweight='bold')\n",
" plt.ylabel('SSE',fontsize=14, fontweight='bold')\n",
" plt.xticks(fontsize=14)\n",
" plt.yticks(fontsize=14)\n",
" plt.plot(list(range(Clustering.min_n_clusters, Clustering.max_n_clusters + 1)), Clustering.kmeans_distortions, marker='o')\n",
" plt.show()\n",
" \n",
" \n",
" @staticmethod\n",
" def silhouette_result(metrics_score:list, algorithm: str):\n",
" plt.figure(figsize=(25, 8))\n",
" sns.set_style(\"darkgrid\")\n",
" plt.title(f'Silhouette score for different number of clusters (algorithm: {algorithm})', fontsize=14, fontweight='bold')\n",
" plt.xlabel('Clusters', fontsize=14, fontweight='bold')\n",
" plt.ylabel('Silhouette', fontsize=14, fontweight='bold')\n",
" plt.xticks(fontsize=14)\n",
" plt.yticks(fontsize=14)\n",
" plt.plot(list(range(Clustering.min_n_clusters, Clustering.max_n_clusters + 1)), metrics_score, marker='o')\n",
" plt.show()\n",
" print('*' * 112)\n",
" \n",
" \n",
" def make_models(self, data_old: pd.DataFrame, data_modified: pd.DataFrame): \n",
" \"\"\"Method for creating models\"\"\"\n",
" for algorithm in Clustering.algorithms: \n",
" # metrics: silhouette score\n",
" metrics = [] \n",
" for n_clusters in range(Clustering.min_n_clusters, Clustering.max_n_clusters + 1): \n",
" if algorithm == 'AgglomerativeClustering':\n",
" ac = AgglomerativeClustering(n_clusters=n_clusters,\n",
" linkage='complete')\n",
" pred = ac.fit_predict(df_transform)\n",
" labels = pd.DataFrame(pred, columns =['Labels'], index=df.index)\n",
" metrics.append(silhouette_score(df_transform, labels))\n",
" Clustering.object_plot(labels, pred, algorithm)\n",
" \n",
" if algorithm == 'SpectralClustering':\n",
" sc = SpectralClustering(n_clusters=n_clusters)\n",
" pred = sc.fit_predict(df_transform)\n",
" labels = pd.DataFrame(pred, columns =['Labels'], index=df.index)\n",
" metrics.append(silhouette_score(df_transform, labels))\n",
" Clustering.object_plot(labels, pred, algorithm)\n",
" \n",
" if algorithm == 'KMeans': \n",
" kmeans = KMeans(n_clusters=n_clusters, \n",
" init=\"k-means++\", \n",
" n_init=10,\n",
" max_iter=280, \n",
" random_state=42)\n",
" pred = kmeans.fit_predict(df_transform) \n",
" labels = pd.DataFrame(pred, columns=['Labels'], index=df.index)\n",
" metrics.append(silhouette_score(df_transform, labels))\n",
" Clustering.kmeans_distortions.append(kmeans.inertia_) \n",
" Clustering.object_plot(labels, pred, algorithm)\n",
" \n",
" if algorithm == 'KMeans':\n",
" Clustering.sse_plot()\n",
" print('*' * 112)\n",
" \n",
" Clustering.silhouette_result(metrics, algorithm)\n",
" \n",
" \n",
" @staticmethod\n",
" def object_plot(labs: pd.DataFrame, predict:np.array, algorithm:str):\n",
" \"\"\"Graphs of the number of objects in a cluster and the silhouette coefficient\"\"\"\n",
" cluster_group = labs.groupby('Labels').size()\n",
" plt.figure(figsize=(25, 5))\n",
" plt.subplot(1, 2, 1)\n",
" sns.set_style(\"darkgrid\")\n",
" plt.title(f'Cluster size (algorithm: {algorithm})', fontsize=14, fontweight='bold')\n",
" sns.barplot(x=cluster_group.values, y=list(map(str, cluster_group.index)))\n",
" plt.xlabel('Number of objects', fontsize=14, fontweight='bold')\n",
" plt.ylabel('Сlusters', fontsize=14, fontweight='bold')\n",
" plt.xticks(fontsize=14)\n",
" plt.yticks(fontsize=14)\n",
" \n",
" plt.subplot(1, 2, 2) \n",
" cluster_labels = np.unique(predict)\n",
" silhouette_vals = silhouette_samples(df_transform, predict, metric='euclidean')\n",
" y_ax_lower, y_ax_upper = 0, 0\n",
" yticks = []\n",
" for i, с in enumerate(cluster_labels):\n",
" c_silhouette_vals = silhouette_vals[predict == с]\n",
" c_silhouette_vals.sort()\n",
" y_ax_upper += len(c_silhouette_vals)\n",
" plt.barh(range(y_ax_lower, y_ax_upper),\n",
" c_silhouette_vals,\n",
" height = 1,\n",
" edgecolor='none')\n",
"\n",
" yticks.append((y_ax_lower + y_ax_upper) / 2)\n",
" y_ax_lower += len(c_silhouette_vals) \n",
" silhouette_avg = np.mean(silhouette_vals)\n",
" plt.axvline(silhouette_avg, color=\"red\", linestyle=\"--\")\n",
" sns.set_style(\"darkgrid\")\n",
" plt.title(f'Silhouette plot for different clusters (algorithm: {algorithm})', fontsize=14, fontweight='bold') \n",
" val = float(''.join([i for i in str(silhouette_avg)][0:5]))\n",
" plt.xlabel(f'Silhouette = {val}', fontsize=14, fontweight='bold')\n",
" plt.ylabel('Clusters', fontsize=14, fontweight='bold')\n",
" plt.yticks(yticks, cluster_labels, fontsize=14)\n",
" plt.show()\n",
" print('*' * 112)\n",
" \n",
"\n",
"clustering = Clustering()\n",
"clustering.make_models(df, df_transform)"
]
},
{
"cell_type": "markdown",
"id": "6234c083",
"metadata": {
"papermill": {
"duration": 0.058793,
"end_time": "2022-08-28T13:52:29.521183",
"exception": false,
"start_time": "2022-08-28T13:52:29.462390",
"status": "completed"
},
"tags": []
},
"source": [
"# What can we say from these graphs: \n",
"* Good metric results for Agglomerative and Spectral Clustering algorithms \n",
"* Best Model: AgglomerativeClustering(n_clusters=3)."
]
},
{
"cell_type": "markdown",
"id": "a0db62cd",
"metadata": {
"papermill": {
"duration": 0.056866,
"end_time": "2022-08-28T13:52:29.635184",
"exception": false,
"start_time": "2022-08-28T13:52:29.578318",
"status": "completed"
},
"tags": []
},
"source": [
"# Now let's build graphs of the main characteristics for features int | float for the best models (since we got object labels)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "10d77a73",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:52:29.756771Z",
"iopub.status.busy": "2022-08-28T13:52:29.756257Z",
"iopub.status.idle": "2022-08-28T13:52:29.939735Z",
"shell.execute_reply": "2022-08-28T13:52:29.938377Z"
},
"papermill": {
"duration": 0.247759,
"end_time": "2022-08-28T13:52:29.942709",
"exception": false,
"start_time": "2022-08-28T13:52:29.694950",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"best_model = AgglomerativeClustering(n_clusters=3)\n",
"labels = pd.DataFrame(best_model.fit_predict(df_transform), columns=['Clusters'], index=df.index)\n",
"df = pd.concat([df, labels], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "c474828f",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:52:30.058532Z",
"iopub.status.busy": "2022-08-28T13:52:30.057754Z",
"iopub.status.idle": "2022-08-28T13:52:30.065701Z",
"shell.execute_reply": "2022-08-28T13:52:30.064655Z"
},
"papermill": {
"duration": 0.070059,
"end_time": "2022-08-28T13:52:30.068513",
"exception": false,
"start_time": "2022-08-28T13:52:29.998454",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"object_cols = ['Education', 'Kidhome', 'Teenhome', 'Family_Size', 'Alone', 'Partner']\n",
"num_cols = ['Income', 'Recency', 'MntWines',\n",
" 'MntFruits', 'MntMeatProducts', 'MntFishProducts',\n",
" 'MntSweetProducts', 'MntGoldProds', 'NumDealsPurchases',\n",
" 'NumWebPurchases', 'NumCatalogPurchases','NumStorePurchases',\n",
" 'NumWebVisitsMonth', 'Age', 'Years_Since_Registration',\n",
" 'Sum_Mnt', 'Num_Accepted_Cmp', 'Num_Total_Purchases']"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "4e349f89",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-28T13:52:30.193718Z",
"iopub.status.busy": "2022-08-28T13:52:30.193231Z",
"iopub.status.idle": "2022-08-28T13:52:44.046400Z",
"shell.execute_reply": "2022-08-28T13:52:44.044829Z"
},
"papermill": {
"duration": 13.91993,
"end_time": "2022-08-28T13:52:44.049878",
"exception": false,
"start_time": "2022-08-28T13:52:30.129948",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1800x2160 with 18 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1800x2160 with 18 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1800x720 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def graph_builder():\n",
" \"\"\"Graphs of the main characteristics for features int | float for the best models.\n",
" The best model was chosen based on the results of the graphs\"\"\"\n",
" def boxplot():\n",
" n_cols = len(num_cols)\n",
" rows = n_cols // 3 \n",
" _, axes = plt.subplots(ncols=3, nrows=rows, figsize=(25, 5 * rows))\n",
" plt.subplots_adjust(wspace=0.3, hspace=0.4)\n",
" for num, col in enumerate(num_cols):\n",
" b = sns.boxplot(\n",
" data=df,\n",
" y=col,\n",
" x=\"Clusters\",\n",
" palette=\"Paired\",\n",
" ax=axes.reshape(-1)[num])\n",
" b.set_xlabel('Clusters', fontsize=14)\n",
" b.set_ylabel(f'{df[col].name}', fontsize=14)\n",
" sns.set_style('darkgrid')\n",
" \n",
" def histplot(): \n",
" n_cols = len(num_cols)\n",
" rows = n_cols // 3\n",
" _, axes = plt.subplots(ncols=3, nrows=rows, figsize=(25, 5 * rows))\n",
" plt.subplots_adjust(wspace=0.3, hspace=0.4)\n",
" for num, col in enumerate(num_cols):\n",
" h = sns.histplot(\n",
" data=df,\n",
" x=col,\n",
" hue=\"Clusters\",\n",
" multiple=\"stack\",\n",
" kde=True,\n",
" palette=\"Paired\",\n",
" ax=axes.reshape(-1)[num])\n",
" \n",
" h.set_xlabel(f'{df[col].name}', fontsize=14)\n",
" h.set_ylabel('Count', fontsize=14)\n",
" sns.set_style('darkgrid')\n",
" \n",
" def barplot():\n",
" n_cols = len(object_cols)\n",
" rows = n_cols // 3 + n_cols % 3\n",
" size_cluster = df.groupby(\"Clusters\").count().iloc[:, 0]\n",
" _, axes = plt.subplots(ncols=3, nrows=rows, figsize=(25, 5 * rows))\n",
" plt.subplots_adjust(wspace=0.3, hspace=0.4) \n",
" for num, col in enumerate(object_cols):\n",
" data = (df.groupby([\"Clusters\"])[col].value_counts().unstack(fill_value=0).T)\n",
" data = pd.DataFrame(data.unstack())\n",
" data.columns = [\"Number of objects\"]\n",
" data = data.reset_index()\n",
" b= sns.barplot(\n",
" data=data,\n",
" x=data[col],\n",
" hue=data.Clusters,\n",
" y=data['Number of objects'],\n",
" ax=axes.reshape(-1)[num])\n",
" b.set_xlabel(f'{data[col].name}', fontsize=14)\n",
" b.set_ylabel('Number of objects', fontsize=14)\n",
" sns.set_style('darkgrid')\n",
" \n",
" boxplot()\n",
" histplot()\n",
" barplot()\n",
" \n",
"graph_builder()"
]
},
{
"cell_type": "markdown",
"id": "bae4e70e",
"metadata": {
"papermill": {
"duration": 0.068264,
"end_time": "2022-08-28T13:52:44.191676",
"exception": false,
"start_time": "2022-08-28T13:52:44.123412",
"status": "completed"
},
"tags": []
},
"source": [
"## 6) Business Recommendations"
]
},
{
"cell_type": "markdown",
"id": "3804fd18",
"metadata": {
"papermill": {
"duration": 0.066646,
"end_time": "2022-08-28T13:52:44.325998",
"exception": false,
"start_time": "2022-08-28T13:52:44.259352",
"status": "completed"
},
"tags": []
},
"source": [
"# As a result of cluster analysis, we received three groups of buyers (clusters):\n",
"# 0 cluster:\n",
"* Middle income people (average income equals 50000)\n",
"* Average age is 52 years\n",
"* Have an education (Graduation, 2n Cycle, Master, PhD)\n",
"* People without family, people with families with and without children\n",
"* Quite often buy wines, but they also often buy meat\n",
"* Most often make purchases on the web\n",
"* The average number of purchases is 13\n",
"\n",
"# 1 cluster:\n",
"* High income people (average income equals 70000)\n",
"* Average age is 55 years\n",
"* Have an education (Graduation, 2n Cycle, Master, PhD)\n",
"* Have a family with children (Teenhome)\n",
"* Quite often buy wines, but they also often buy meat\n",
"* Most often make purchases in the stores themselves\n",
"* Most often make purchases (compared to other clusters)\n",
"\n",
"# 2 cluster:\n",
"* Average income equals 38000 \n",
"* Average age is 49 years\n",
"* Have an education (Graduation, 2n Cycle, Master, PhD)\n",
"* People with families with and without children\n",
"* A low number of purchases and, accordingly, spend little money on purchases "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
},
"papermill": {
"default_parameters": {},
"duration": 172.940742,
"end_time": "2022-08-28T13:52:46.152462",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2022-08-28T13:49:53.211720",
"version": "2.3.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}