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https://youtu.be/sa2ydCknVR8?si=YXEucjOtMKsEiPnA
https://youtu.be/60fxBIZ1ueA?si=NiHtkhlR1gWGa0YV
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🚀 Purpose: Analyze the Electric Vehicle (EV) market in India from 2001 to 2024. Highlight the most significant growth regions, trends, and key drivers to support investment and policy initiatives.
♻️ Data Source: Kaggel
🔍Key Objectives: Identify trends in EV adoption and compare growth across vehicle categories. Highlight states and regions with the highest EV market penetration. Examine the impact of government policies on market growth.
Data Structure: ❕ Columns include State/UT, Year, EV Sales, EV Production, Charging Stations, EV Categories (Two-Wheelers, Cars, Buses, etc.), Government Incentives, and other related metrics.
🧹Data Cleaning: Duplicate removal, handling missing values, renaming columns, and converting data types for consistency.
👨💻Exploratory Data Analysis (EDA): ◻️ Summarizing data with statistics, identifying trends through visualizations, and detecting outliers.
Visualization Techniques: 📈📉 Line Charts for time-series trends in EV sales and production. Bar Plots for comparing EV sales and production across states and categories. Pie Charts for proportional representation of EV categories.
Findings and Conclusion: 🚕 Identified key trends in EV adoption, leading states, and impact of government policies. Provided policy recommendations to boost EV adoption and infrastructure development. Suggestions for future research and deeper insights into the EV ecosystem.
Flowchart 🚎 Start: Data Source Objective: Analyze EV market trends over time.
🌊 Data Cleaning Process:
Remove duplicates: Eliminate redundant entries to ensure accurate analysis. Handle missing data: Use imputation or remove incomplete rows for clean data. Rename columns: Ensure consistent, descriptive column names. Convert data types: Convert columns to appropriate types (e.g., numeric, categorical). 🗝️ Exploratory Data Analysis (EDA):
Summarize data: Provide descriptive statistics to understand the distribution of EV sales, production, etc. Visualize trends: Use histograms, scatter plots, or line charts to show relationships. Detect outliers: Identify unusual data points that could affect analysis. 🚀 Trend Identification & Visualization Techniques:
Line Charts (Time-Series): Display changes in EV sales/production over time, highlighting patterns and growth spurts. Bar Plots (Category-wise): Compare sales and production across states, categories, and vehicle types. Pie Charts (Proportions): Show the market share of different EV categories (two-wheelers, cars, etc.). 🥀 Conclusion & Recommendations:
Key trends identified: Summarize the most important findings, such as spikes in sales or EV category growth. Regional analysis: Identify regions with higher EV adoption and government incentives. Policy suggestions: Offer actionable recommendations for boosting EV adoption based on the analysis. 🔍 Future Research: Explore further research areas, such as integrating battery technology advancements, consumer behavior insights, and future policy impacts on the market.
🔥 Summary of the Project:
This project focuses on analyzing State/UT-wise EV market data in India from 2001 to 2024. It aims to uncover patterns in EV sales, production, and infrastructure development while highlighting sensitive regions and critical periods. By leveraging data visualization techniques, this research offers insights into market trends and provides recommendations for policymakers, industry players, and investors to further accelerate the EV market. .
