The project aims to conduct an Exploratory Data Analysis (EDA) on a dataset containing information about car features and Manufacturer's Suggested Retail Price (MSRP). The analysis will be performed using Python, leveraging essential libraries such as NumPy, Seaborn, and Matplotlib.
The dataset comprises various attributes related to cars, including features like make, model, year, horsepower, fuel efficiency, and MSRP. The primary focus is on understanding the relationships and patterns within the data.
- Identify and handle missing data.
- Explore the distribution of car features.
- Examine the relationship between different features and MSRP.
- Visualize patterns and trends in the dataset.
- Python: The primary programming language for data analysis.
- NumPy: Used for numerical operations and handling arrays.
- Seaborn: Employed for creating visually appealing statistical graphics.
- Matplotlib: Utilized for creating static, animated, and interactive visualizations.
- Data Loading: Import the dataset into a Pandas DataFrame.
- Data Cleaning: Address missing values and handle any inconsistencies in the data.
- Descriptive Statistics: Obtain summary statistics to understand the central tendency and variability of the features.
- Visualization:
- Use Matplotlib and Seaborn to create histograms, box plots, and scatter plots to visualize the distribution of features.
- Explore pairwise relationships between features and the target variable (MSRP).
- Correlation Analysis: Utilize correlation matrices and heatmaps to identify relationships between numerical variables.
- Outlier Detection: Identify and handle outliers that may affect the overall analysis.
- Histograms: Display the distribution of numerical features.
- Box Plots: Visualize the spread of features and identify potential outliers.
- Scatter Plots: Explore the relationship between individual features and MSRP.
- Heatmaps: Illustrate correlations between features.