Toyota Analysis
This project focuses on analyzing a dataset of 'Toyota' vehicles to uncover insights and trends using data science techniques.
The main goal of the project is to provide valuable information for decision-making,
such as pricing strategies, market trends, and feature significance.
Features:
Data Cleaning and Preprocessing: Handled missing values, outliers, and inconsistencies in the dataset to ensure data quality.
Exploratory Data Analysis (EDA): Conducted in-depth analysis to identify patterns, trends, and correlations between vehicle features and their pricing.
Visualization: Created clear and interactive visualizations using libraries like Matplotlib and Seaborn to present data insights effectively.
Statistical Analysis: Performed regression analysis to determine the key factors influencing vehicle prices.
Model Development: Built and evaluated predictive models to estimate prices based on vehicle attributes.
Highlights:
Successfully identified key factors influencing vehicle pricing, such as mileage, age, and additional features. Developed a predictive model with high accuracy for price estimation.
Use Cases:
Informing pricing strategies for vehicle dealerships.
Understanding market trends for used car sales.
Assisting buyers in evaluating the fair value of vehicles.
Features:
Data Cleaning and Preprocessing: Handled missing values, outliers, and inconsistencies in the dataset to ensure data quality.
Exploratory Data Analysis (EDA): Conducted in-depth analysis to identify patterns, trends, and correlations between vehicle features and their pricing.
Visualization: Created clear and interactive visualizations using libraries like Matplotlib and Seaborn to present data insights effectively.
Statistical Analysis: Performed regression analysis to determine the key factors influencing vehicle prices.
Model Development: Built and evaluated predictive models to estimate prices based on vehicle attributes.
Highlights:
Successfully identified key factors influencing vehicle pricing, such as mileage, age, and additional features. Developed a predictive model with high accuracy for price estimation.
Use Cases:
Informing pricing strategies for vehicle dealerships.
Understanding market trends for used car sales.
Assisting buyers in evaluating the fair value of vehicles.
Technology Stack: Python, Beautiful Soup, Pandas, NumPyScikit-Learn and Jupyter Notebook, ML model
GitHub repo