im-dpaul / EDA-Cars-Data

Analyze cars dataset through EDA using Python. Includes data preprocessing, visualization, and insights extraction. Technologies: Pandas, Seaborn, Matplotlib.

Exploratory Data Analysis (EDA) on Cars Data

This project conducts Exploratory Data Analysis (EDA) on a dataset containing information about cars. It covers various aspects of data preprocessing, visualization, and analysis to derive insights into the characteristics and trends of the cars.

Table of Contents

• Introduction
• Steps
  1. Importing Libraries
  2. Loading Data and Data Understanding
  3. Data Cleaning
  4. Detecting Outliers
  5. Visualization

Introduction

This project aims to explore and analyze a dataset related to cars, uncovering insights into various attributes such as price, horsepower, and cylinders. By performing EDA, we gain a deeper understanding of the dataset and its underlying patterns.

Steps

1. Importing Libraries

• Pandas: Used for data manipulation and analysis.
• NumPy: Utilized for numerical computations.
• Seaborn: Employed for data visualization.
• Matplotlib: Utilized for creating plots and charts.

2. Loading Data and Data Understanding

• The "cars_data.csv" file is loaded into a pandas DataFrame (df).
• The first and last five rows (df.head(5), df.tail(5)) are displayed to get a glimpse of the data structure.
• Basic data information (df.info()) is explored, including data types (df.dtypes) and descriptive statistics (df.describe()).

3. Data Cleaning

• Irrelevant columns ('Engine Fuel Type', 'Market Category', etc.) are dropped (df.drop(...)).
• Column names are made more descriptive (df.rename(...)).
• Duplicate rows are identified (duplicate_rows_df) and removed (df.drop_duplicates()).
• Missing values (df.isna().sum()) are examined and dropped (df.dropna()).

4. Detecting Outliers

• Boxplots are generated (sns.boxplot(...)) to visualize potential outliers in columns like price and horse power.
• Interquartile Range (IQR) is calculated to define outlier thresholds.
• Rows with values outside the outlier range (IQR) are removed.

5. Visualization

• A bar chart (plt.bar(...)) shows the distribution of car makes.
• A heatmap (sns.heatmap(...)) visualizes correlations between numerical features.
• A scatter plot (plt.scatter(...)) explores the relationship between horsepower and price.