Customer Personality Analysis training + flask app

Marketing-Campaign/.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
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+# Unit test / coverage reports
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+*.cover
+*.py,cover
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+# Translations
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+# Django stuff:
+*.log
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+instance/
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+# PyBuilder
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+# Jupyter Notebook
+.ipynb_checkpoints
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+profile_default/
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+
+# pyenv
+.python-version
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+# SageMath parsed files
+*.sage.py
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+# Environments
+.env
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Marketing-Campaign/.vscode/settings.json

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+{
+    "liveServer.settings.port": 5501
+}

Marketing-Campaign/README.md

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+# Customer Personality Analysis
+
+Customer Personality Analysis is a detailed analysis of a company's ideal customers. It helps a business to better understand its customers and makes it easier for them to modify products according to the specific needs, behaviors and concerns of different types of customers.
+
+Customer personality analysis helps a business to modify its product based on its target customers from different types of customer segments
+
+**For example**, instead of spending money to market a new product to every customer in the company's database, a company can analyze which customer segment is most likely to buy the product and then market the product only on that particular segment.
+
+## Objectives
+
+1. What people say about your product: what gives customers attitude towards the product
+2. What people do: which reveals what people are doing rather than what they are saying about your product.
+
+## How to Run
+
+*Step 1 - Copying repo in local machine*
+```bash
+git clone   https://github.com/DSCVITBHOPAL/ML-Reserve.git
+```
+*Step 2 - Navigate inside the Customer-Personality-Analysis directory.*
+
+*Step 3 - Create a conda environment*
+```bash
+conda create -p env python==3.7.6 -y
+```
+<p align="center">or</p>
+
+```bash
+conda create --prefix ./env python=3.7 -y
+```
+*Step 4 - Activate the conda environment*
+```bash
+conda activate env/
+```
+<p align="center">or</p>
+
+```bash
+source activate ./env
+```
+*Step 5 - Installing dependencies*
+```bash
+pip install -r requirements.txt
+```
+
+*Step 6 - Run your app.py*
+```bash
+python app.py
+```

Marketing-Campaign/app.py

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+import os
+import sys
+import pickle
+from flask import Flask, render_template, request
+import numpy as np
+import pandas as pd
+from customer_personality.logger.log import logging
+from customer_personality.exception.exception_handler import AppException
+from customer_personality.config.configuration import AppConfiguration
+from customer_personality.pipeline.training_pipeline import TrainingPipeline
+
+app = Flask(__name__) # initializing a flask app
+
+@app.route('/',methods=['GET'])  # route to display the home page
+def homePage():
+    try:
+        return render_template("index.html")
+    except Exception as e:
+            raise AppException(e, sys) from e
+
+
+
+@app.route('/train',methods=['POST','GET'])
+def train():
+    if request.method == 'POST':
+        try:
+            obj = TrainingPipeline()
+            obj.start_training_pipeline()
+            logging.info("Training Completed!")
+            return render_template("index.html")
+
+        except Exception as e:
+            raise AppException(e, sys) from e
+
+
+
+@app.route('/predict',methods=['POST','GET']) # route to show the predictions in a web UI
+def predict():
+    if request.method == 'POST':
+        try:
+            #  reading the inputs given by the user
+            Income =float(request.form['Income'])
+            Recency =int(request.form['Recency'])
+            Age =int(request.form['Age'])
+            TotalSpendings =int(request.form['TotalSpendings'])
+            Children =int(request.form['Children'])
+            MonthEnrollement =int(request.form['MonthEnrollement'])
+
+            data = [Income,Recency,Age,TotalSpendings,Children,MonthEnrollement]
+            model = pickle.load(open(AppConfiguration().get_prediction_config().trained_model_path,'rb'))
+            output = model.predict([data])[0]
+            print(output)
+
+            return render_template('results.html', prediction = str(output))
+
+        except Exception as e:
+            raise AppException(e, sys) from e
+
+    else:
+        return render_template('index.html')
+
+
+
+if __name__ == "__main__":
+	app.run(host="127.0.0.1", port=5003,debug=True)

Marketing-Campaign/config/config.yaml

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+artifacts_config:
+  artifacts_dir: artifacts
+
+data_ingestion_config:
+  dataset_download_url: https://publicdatasetvaasu.s3.us-east-2.amazonaws.com/dataset.zip
+  dataset_dir: dataset
+  ingested_dir: ingested_data
+  raw_data_dir: raw_data
+
+
+data_validation_config:
+  clean_data_dir: clean_data
+  serialized_objects_dir: serialized_objects
+  marketing_campaign_csv_file: marketing_campaign.csv
+
+data_transformation_config:
+  transformed_data_dir: transformed_data
+
+model_trainer_config:
+  trained_model_dir: trained_model
+  trained_model_name: model.pkl

Marketing-Campaign/customer_personality/components/data_ingestion.py

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+import os
+import sys
+from six.moves import urllib
+import zipfile
+from customer_personality.logger.log import logging
+from customer_personality.exception.exception_handler import AppException
+from customer_personality.config.configuration import AppConfiguration
+
+
+
+class DataIngestion:
+
+    def __init__(self, app_config = AppConfiguration()):
+        """
+        DataIngestion Intialization
+        data_ingestion_config: DataIngestionConfig 
+        """
+        try:
+            logging.info(f"{'='*20}Data Ingestion log started.{'='*20} ")
+            self.data_ingestion_config= app_config.get_data_ingestion_config()
+        except Exception as e:
+            raise AppException(e, sys) from e
+
+
+    def download_data(self):
+        """
+        Fetch the data from the url
+
+        """
+        try:
+            dataset_url = self.data_ingestion_config.dataset_download_url
+            zip_download_dir = self.data_ingestion_config.raw_data_dir
+            os.makedirs(zip_download_dir, exist_ok=True)
+            data_file_name = os.path.basename(dataset_url)
+            zip_file_path = os.path.join(zip_download_dir, data_file_name)
+            logging.info(f"Downloading data from {dataset_url} into file {zip_file_path}")
+            urllib.request.urlretrieve(dataset_url,zip_file_path)
+            logging.info(f"Downloaded data from {dataset_url} into file {zip_file_path}")
+            return zip_file_path
+
+        except Exception as e:
+            raise AppException(e, sys) from e
+
+
+    def extract_zip_file(self,zip_file_path: str):
+        """
+        zip_file_path: str
+        Extracts the zip file into the data directory
+        Function returns None
+        """
+        try:
+            ingested_dir = self.data_ingestion_config.ingested_dir
+            os.makedirs(ingested_dir, exist_ok=True)
+            with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
+                zip_ref.extractall(ingested_dir)
+            logging.info(f"Extracting zip file: {zip_file_path} into dir: {ingested_dir}")
+        except Exception as e:
+            raise AppException(e,sys) from e
+
+
+    def initiate_data_ingestion(self):
+        try:
+            zip_file_path = self.download_data()
+            self.extract_zip_file(zip_file_path=zip_file_path)
+            logging.info(f"{'='*20}Data Ingestion log completed.{'='*20} \n\n")
+        except Exception as e:
+            raise AppException(e, sys) from e
+

Marketing-Campaign/customer_personality/components/data_transformation.py

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+import os
+import sys
+import pandas as pd
+from customer_personality.logger.log import logging
+from customer_personality.exception.exception_handler import AppException
+from customer_personality.config.configuration import AppConfiguration
+
+
+
+class DataTransformation:
+    def __init__(self, app_config = AppConfiguration()):
+        try:
+            self.data_transformation_config = app_config.get_data_transformation_config()
+        except Exception as e:
+            raise AppException(e, sys) from e
+
+
+
+    def get_data_transformer(self):
+        try:
+            df = pd.read_csv(self.data_transformation_config.clean_data_file_path)
+            final_df = df.drop(
+                [
+                'ID', 'Year_Birth','Education','Marital_Status', 'Kidhome', 
+                'Teenhome', 'Dt_Customer', 'MntWines', 'MntFruits', 'MntMeatProducts',
+                    'MntFishProducts', 'MntSweetProducts','MntGoldProds','NumDealsPurchases',
+                    'NumWebPurchases', 'NumCatalogPurchases', 'NumStorePurchases','NumWebVisitsMonth',
+                    'AcceptedCmp3','AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',
+                    'AcceptedCmp2', 'Complain', 'Z_CostContact', 'Z_Revenue', 'Response','AgeGroup'
+                ],
+
+                axis = 1
+            )
+
+            #saving personality data
+            os.makedirs(self.data_transformation_config.transformed_data_dir, exist_ok=True)
+            final_df.to_csv(os.path.join(os.path.join(self.data_transformation_config.transformed_data_dir),"transformed_data.csv"), index = False)
+            logging.info(f"Saved personality data to {self.data_transformation_config.transformed_data_dir}")
+
+
+        except Exception as e:
+            raise AppException(e, sys) from e
+
+
+    def initiate_data_transformation(self):
+        try:
+            logging.info(f"{'='*20}Data Transformation log started.{'='*20} ")
+            self.get_data_transformer()
+            logging.info(f"{'='*20}Data Transformation log completed.{'='*20} \n\n")
+        except Exception as e:
+            raise AppException(e, sys) from e