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’.
- Table of Contents
⚠️ Frameworks and Libraries- 📖 Data Preprocessing
- 🔗 Download
- 🔑 Prerequisites
- 🚀 Installation
- 💡 How to Run
- 🔑 Results
- 👏 And it's done!
- 🙋 Citation
- 🔰 Future Goals
- ❤️ Owner
- 👀 License
- 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 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.
The dataset is now available here !
All the dependencies and required libraries are included in the file requirements.txt See here
- 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
- Open terminal. Go into the cloned project directory and type the following command:
$ python3 evmarket_india.py
Correlation Matrix for the data set using Seaborn
Dendrogram for our data:
ScreePlot for our data:
Cluster analysis using silhouette:
Final Results:
Check out the report here
Feel free to mail me for any doubts/query :email: chagantivenkataramireddy1@gmail.com
You are allowed to cite any part of the code or our dataset. You can use it in your Research Work or Project. Remember to provide credit to the Maintainer Chaganti Reddy by mentioning a link to this repository and her GitHub Profile.
Follow this format:
- Author's name - Chaganti Reddy
- Date of publication or update in parentheses.
- Title or description of document.
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This study endeavoured to present EV taxonomy using an a-priori approach to categorize potential EV buyers into ub-segments of young and educated consumers and tested eticulously with a blended approach of multivariate and bivariate techniques
Made with ❤️ by Chaganti Reddy
MIT © Chaganti Reddy