Project 1

Next Steps:

1. Handle missing values using appropriate imputation techniques.
2. Standardize and clean categorical & numerical features.
3. Perform descriptive analytics (summary statistics, distributions, etc.).

The Automobile 1985 dataset has been successfully cleaned, and there are no missing values remaining.

Next Steps:

1. Perform Descriptive Analytics:
• Summary statistics (mean, median, mode, standard deviation, etc.).
• Distribution analysis for numerical and categorical features.
• Correlation between variables.

Descriptive Analytics Insights:

1. Numerical Summary:

   • Provides key statistics such as mean, standard deviation, min, max, and quartiles for numerical variables.
   • Identifies range and spread of data, helping to detect outliers.

2. Categorical Summary:

   • Shows unique values, most common category (mode), and frequency of each categorical variable.
   • Example:
     • Most common car make: Toyota (32 occurrences).
     • Most common body style: Sedan (94 occurrences).
     • Most common drive-wheel type: Front-Wheel Drive (118 occurrences).

Next Steps:

• Visualize distributions of numerical and categorical data.
• Explore correlations between variables.