Electric Vehicle Market Segmentation

Market segmentation becomes a crucial tool for evolving transportation technology such as electric vehicles (EVs) in emerging markets to explore and implement for extensive adoption. EVs adoption is expected to grow phenomenally in near future as low emission and low operating cost vehicle, and thus, it drives a considerable amount of forthcoming academic research curiosity. The main aim of this study is to explore and identify distinct sets of potential buyer segments for EVs based on psychographic, behavioral, and socio-economic characterization by employing an integrated research framework of ‘perceived benefits-attitude-intention’

⚠️ Frameworks and Libraries

SKLearn: Simple and efficient tools for predictive data analysis
Seaborn: Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
Plotly: The plotly Python library is an interactive, open-source plotting library that supports over 40 unique chart types covering a wide range of statistical, financial, geographic, scientific, and 3-dimensional use-cases.
KElbowVisualizer: The KElbowVisualizer implements the “elbow” method to help data scientists select the optimal number of clusters by fitting the model with a range of values for . If the line chart resembles an arm, then the “elbow” (the point of inflection on the curve) is a good indication that the underlying model fits best at that point. In the visualizer “elbow” will be annotated with a dashed line.
Matplotlib : Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
Numpy: Caffe-based Single Shot-Multibox Detector (SSD) model used to detect faces
Pandas: pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.

📖 Data Preprocessing

Data pre-processing is an important step for the creation of a machine learning model. Initially, data may not be clean or in the required format for the model which can cause misleading outcomes. In pre-processing of data, we transform data into our required format. It is used to deal with noises, duplicates, and missing values of the dataset. Data pre-processing has the activities like importing datasets, splitting datasets, attribute scaling, etc. Preprocessing of data is required for improving the accuracy of the model.

🚀 Installation

Clone the repo

$ git clone https://github.com/Chaganti-Reddy/EVMarket-India.git

Change your directory to the cloned repo

$ cd EVMarket-India

Now, run the following command in your Terminal/Command Prompt to install the libraries required

$ pip3 install -r requirements.txt

💡 How to Run

Open terminal. Go into the cloned project directory and type the following command:

$ python3 evmarket_india.py

🔑 Results

Correlation Matrix for the data set using Seaborn

Dendrogram for our data:

EV Market Segmentation