This repository contains the code implementation and analysis for the Market Segmentation of the EV market in India using KMeans Clustering.
To perform the market segmentation for the EV market three datasets with the following sources were used:
- Charging Stations in India: https://www.kaggle.com/datasets/saketpradhan/electric-vehicle-charging-stations-in-india
- EV Population Data of USA: https://catalog.data.gov/dataset/electric-vehicle-population-data
- Indian Automobile Buying Behaviour: https://www.kaggle.com/datasets/karivedha/indian-consumers-cars-purchasing-behaviour
The code implementation involves several steps, including data loading, data preprocessing, feature engineering, exploratory data analysis (EDA), and clustering. The main libraries used in the code are pandas, matplotlib, seaborn, and scikit-learn.
The code is structured as follows:
- Data Loading: The dataset is loaded into separate dataframes.
- Data Preprocessing: Unnecessary columns are dropped, and the dataframes are merged.
- Feature Engineering: Additional features such as weekend/weekday and day of the week are created.
- Exploratory Data Analysis (EDA): Various visualizations are generated to gain insights into the data.
- Clustering: KMeans clustering was used to segment the market into two segments.
This project analyzed the Indian EV market and identified two potential target segments:
- Segment 1: younger, less well-off professionals who are looking for affordable cars
- Segment 2: middle-aged, well-off professionals who are looking for expensive cars.
The project then develops a marketing mix for the ideal target segment, which is younger, less well-off professionals who are looking for affordable cars.
Note: This Readme file provides a summary of the project. For a more comprehensive understanding, please review the code and comments in the Jupyter Notebook.