𝐃𝐚𝐭𝐚𝐬𝐞𝐭 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰
Around 3.34 Lakh passenger cars were sold in the Indian market in May 2023. The sales increased by over 13% when compared to May last year. The Top 25 Selling Cars constituted over 75% of the cars sold in April 2023.
This dataset consists of 141 columns. Perform Exploratory Data analysis on this dataset. Document the findings and insights using proper graphs to represent the data.
You need to perform Univariate and Bivariate analysis for the given dataset. Below are the steps you can follow for both univariate and bivariate analysis of the dataset.
📊 1276 entries, 140 columns 📈 Float64 for numeric, Object for categorical
𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠
✨ Renamed & combined columns
🔄 Converted 'Price' to integer
🧹 Removed missing values & standardized columns
💡 Extracted numeric values from 'Power' & 'Torque'
𝐃𝐚𝐭𝐚 𝐓𝐲𝐩𝐞𝐬
👍 Verified appropriate data types for analysis
𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐯𝐞 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬
To calculate basic descriptive statistics and create a bar chart showcasing counts of observations, makers, models, and features, you'll need a dataset. Assuming you have a dataset, let's go through the steps using Python and some popular libraries like Pandas and Matplotlib📊 Bar chart showcasing counts of observations, makers, models, and features
𝐇𝐢𝐬𝐭𝐨𝐠𝐫𝐚𝐦𝐬
📈 Top 10 cars based on sales & price visualized through histograms
𝐁𝐚𝐫 𝐂𝐡𝐚𝐫𝐭𝐬
🚙 Explored the count of observations for the top 7 cars by body type & model
𝐁𝐨𝐱 𝐏𝐥𝐨𝐭𝐬
📦 Identified outliers & understood mileage and price distributions
𝐏𝐢𝐞 𝐂𝐡𝐚𝐫𝐭𝐬
🍰 Contribution of the top 10 car companies & models visually represented
𝐂𝐨𝐮𝐧𝐭 𝐏𝐥𝐨𝐭𝐬
📊 Visualized count of observations for all variables
𝐁𝐢𝐯𝐚𝐫𝐢𝐚𝐭𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬
🔄 Correlation matrix & various visualizations exploring relationships & distributions
𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐚𝐧𝐝 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧
📝 Documented key findings and insights from univariate & bivariate analyses
