AtliQ Motors India – EV Sales Analysis

📘 Project Overview

AtliQ Motors India is an electric vehicle (EV) manufacturer and distributor seeking to expand its footprint across Indian states. This project performs an end-to-end analysis of EV sales data through Exploratory Data Analysis (EDA) and an interactive Power BI dashboard. The objective is to uncover sales trends, regional performance, growth patterns, and insights for strategic decision-making.

The analysis integrates three datasets:

1. dim_date.csv – Contains fiscal dates and derived calendar features.
2. electric_vehicle_sales_by_makers.csv – Captures EV sales volume by manufacturer, model, and year.
3. electric_vehicle_sales_by_state.csv – Represents total vehicle sales (electric and non-electric) by state and year.

---

🎯 Key Objectives

• Understand EV adoption trends across India from 2022–2024.
• Identify top-performing manufacturers and states.
• Analyze the compound annual growth rate (CAGR) in EV sales.
• Compare EV penetration across states and years.
• Provide actionable insights for policy, production, and distribution planning.

---

🧩 Features and Functionality

1. Exploratory Data Analysis (EDA)

The EDA was conducted in Python using pandas, numpy, matplotlib, and seaborn. Key steps included:

• Data Cleaning: Removal of duplicates, handling of nulls, datatype corrections.
• Feature Engineering: Derivation of fiscal years, quarters, and EV penetration metrics.
• Trend Analysis: Temporal evolution of EV sales across states and makers.
• Correlation Study: Relationship between total vehicles sold and EV share.
• CAGR Computation: State-wise and manufacturer-wise growth rate analysis.

2. Power BI Dashboard

A multi-page Power BI report was built to visualize findings from the EDA. The dashboard includes:

• KPI Page: Total EVs sold, YoY growth, CAGR, and EV penetration ratio.
• Geographical View: State-wise EV adoption and market size heatmap.
• Maker Comparison: Top 10 manufacturers by cumulative EV sales.
• Trend View: Quarterly and yearly EV growth visualization.
• State Insights Page: Drill-down filters for each state showing detailed trend and growth patterns.

---

📊 Key Insights

From EDA

• Data Quality: Minimal nulls, with consistent fiscal-year formatting.
• EV Growth: EV sales grew significantly post-2022, with several states showing >80% CAGR.
• Top States: Maharashtra, Karnataka, and Tamil Nadu lead in total EV sales volume.
• Emerging Markets: Northeastern and smaller states show strong growth momentum despite lower base.
• Manufacturer Trends: Tata Motors consistently leads in total EV sales, followed by MG Motors and Mahindra.
• EV Penetration: The ratio of EVs to total vehicles increased steadily, surpassing 4% national average by 2024.

From Power BI Dashboard

• CAGR Leaders: Gujarat, Kerala, and Delhi showed exceptional EV growth between 2022–2024.
• Sales Concentration: Top 5 states account for ~60% of total EV sales nationwide.
• Yearly Seasonality: Sales tend to peak in Q3 (festive and policy rollout periods).
• Maker Diversity: New entrants such as Hyundai and Kia exhibit sharp growth curves.
• Geographical Patterns: Southern and Western India remain dominant EV markets, while Eastern regions catch up.

---

⚙️ Installation & Setup Instructions

For EDA (Python Environment)

# Clone this repository
git clone https://github.com/<your-username>/AtliQ-Motors-EV-Sales-Analysis.git
cd AtliQ-Motors-EV-Sales-Analysis

# Create virtual environment
python -m venv venv
venv\Scripts\activate     # (Windows)
# or
source venv/bin/activate    # (Mac/Linux)

# Install dependencies
pip install -r requirements.txt

# Run Jupyter Notebook
jupyter notebook EDA.ipynb

For Power BI

1. Open AtliQ_EV_Sales.pbix file in Power BI Desktop.
2. Ensure data source paths match local CSV/Excel dataset paths.
3. Refresh data and view the full dashboard.

---

🧠 Tech Stack & Dependencies

• Languages: Python (for EDA), DAX & M (for Power BI)
• Libraries: pandas, numpy, matplotlib, seaborn, plotly
• Tools: Power BI Desktop, Jupyter Notebook, Git
• Data Sources: CSV files – dim_date, electric_vehicle_sales_by_makers, electric_vehicle_sales_by_state

---

📁 File Structure

AtliQ-Motors-EV-Sales-Analysis/
│
├── data/
│   ├── dim_date.csv
│   ├── electric_vehicle_sales_by_makers.csv
│   ├── electric_vehicle_sales_by_state.csv
│
├── EDA.ipynb                     # Exploratory Data Analysis notebook
├── EV_Sales_Analysis.pbix           # Power BI dashboard file
├── README.md                     # Project documentation
├── requirements.txt              # Python dependencies
└── assets/                       # Screenshots of Power BI dashboards

---

👤 Author

Harddik Singh
Data Analyst | Power BI Developer | Python & SQL Enthusiast
📧 Email: harddik05@gmail.com

🪪 License

This project is licensed under the MIT License. Feel free to use, modify, and distribute with proper attribution.