EV Market Exploratory Data Analysis 🚗⚡ 👉🏻 Conducted an in-depth analysis of the rapidly growing Electric Vehicle (EV) market using data visualization techniques, addressing key areas like consumer behavior, product optimization, and strategic planning. 1️⃣ Data Transformation: 👉🏻 Cleaned and processed 3 datasets (ev_main, ev_efficiency, ev_manufacturing) involving 1,000+ records to ensure consistency and reliability. 👉🏻 Resolved brand-model inconsistencies (e.g., fixing a critical 16-brand duplication issue for the BMW i3) using custom mapping and validation. 2️⃣ Visualization Insights: 👉🏻 Built 7+ impactful visualizations including: - Scatter plots analyzing the price vs. range relationship across 50+ EV models. - Bar plots comparing brand-wise efficiency, manufacturing locations, and price segmentation. - Box plots revealing price distribution across top 10 brands. - Pie charts highlighting sales-based market shares. - Time-series plots simulating launch trends to identify peak innovation periods. 3️⃣ Impact: 👉🏻 Identified that 20% of brands control nearly 65% of the EV market based on sales distribution. 👉🏻 Uncovered a positive correlation (~70%) between vehicle range and pricing, influencing strategic product positioning. 👉🏻 Highlighted efficiency leaders where top brands achieved 10–15% higher range per unit price compared to industry averages. 4️⃣ Tools & Technologies: 👉🏻 Python (Pandas, Matplotlib, Seaborn) 👉🏻 Data Cleaning & Feature Engineering 👉🏻 Advanced Data Visualization 👉🏻 Through this project, I strengthened my skills in data storytelling 📊, problem-solving, and strategic data-driven decision-making, positioning me to deliver impactful insights in real-world business scenarios.