The Monthly Car Sales Analysis project uses data analytics, machine learning, and time series forecasting to track and predict car sales trends. It explores patterns by brand, model, region, and season, identifies consumer preferences, and generates insights that support marketing, inventory, and strategic decision-making in the automobile industry

The project aims to perform various visualizations and provide various insights from the considered Indian automobile dataset by performing data analysis that utilizing machine learning algorithms in R programming language.

Automobile Data Insights: A data analysis project exploring trends in vehicle prices, fuel efficiency, and other key attributes. Uses Python, Pandas, Matplotlib, and Seaborn for data visualization and insights.

Welcome to the SQL Data Analysis Repository! This repository hosts a collection of SQL scripts and queries for various data analysis tasks. These scripts are designed to help you analyze and gain insights from datasets related to automobiles, sales, and medicine. Below, you'll find a brief overview of the analysis projects available in this repo

The Monthly Car Sales Analysis project uses data analytics, machine learning, and time series forecasting to track and predict car sales trends. It explores patterns by brand, model, region, and season, identifies consumer preferences, and generates insights that support marketing, inventory, and strategic decision-making in the automobile industry

This project focuses on analyzing the performance of luxury automobile brands across Europe and the US. The analysis includes key performance metrics such as sales volume, market share, fuel efficiency, engine power, and customer satisfaction. The data-driven insights help identify trends in consumer preferences, technological advancements & more.

Practice Module from the IBM Data Analyst Certification (Coursera)

SQL & Data Analysis project on Automobiles dataset

Data analysis project about Automobile Sales database, mainly using Excel; SQL and Power BI

An exploratory data analysis project examining the impact of economic recessions on automobile sales using simulated historical data.

This project aims to analyze and predict automobile prices using exploratory data analysis (EDA) and machine learning techniques in R.

Automobile Data Analysis with Python" is a focused project that leverages Python for in-depth analysis of automotive data, offering valuable insights into vehicle performance, efficiency, and trends.

This project involves comprehensive analysis of automobile data to predict various factors such as price and symboling (insurance risk rating) using machine learning models. The analysis includes data cleaning, exploratory data analysis (EDA), principal component analysis (PCA), correlation analysis, and predictive modeling using random forests.

This project focuses on performing Exploratory Data Analysis (EDA) on an automobile dataset using Python. The analysis includes data preprocessing, univariate and multivariate analysis, and visualizations to gain insights into the dataset's features and relationships.

Data cleaning project: Pandas analysis of messy automobile dataset. Fixed missing prices with mean imputation, removed duplicates. Jupyter notebook + raw/cleaned CSV files.

Database & Analytics Programming Project - Detailed Analysis of Insurance Industry - Automobile, Healthcare and Travel Insurances. This project includes two structured CSV datasets for healthcare and travel insurance, along with an unstructured JSON file containing automobile data. MongoDB, PostgresSQL, Docker, Python are used.

A comprehensive data analysis project aimed at uncovering insights into customer preferences, pricing sensitivity, and vehicle model trends for Austo Automobiles. The analysis supports data-driven marketing and sales decisions by identifying key customer segments and behavioral patterns.

Transaport-Data is an R-based data analysis project focused on exploring and modeling automobile data, with an emphasis on vehicle pricing and characteristics. The repository includes scripts for statistical analysis, data visualization, and regression modeling. It processes vehicle datasets to extract insights on central tendency

This project, completed as part of my internship at Unified Mentor Pvt Ltd, involves a comprehensive analysis of a dataset containing information about various car models. The analysis focused on data cleaning, transformation, and exploratory data analysis (EDA) to uncover patterns, relationships, and trends in the automobile industry.

This project analyzes the 'Auto MPG' dataset to investigate the impact of automobile characteristics like vehicle weight on fuel efficiency, using Python tools such as Seaborn and Matplotlib for data visualization and analysis.

The Auto Stock Data Analysis of Tata Motors, Hero Motors, Apollo Tyres, and TVS Motors project is an analysis of the historical stock data of these four Indian automobile companies. The project aims to identify trends in the stock prices, compare the performance of the different companies, and develop a trading strategy.

his project focuses on predicting automobile prices using data analysis and machine learning. By following a structured data science workflow, it aims to build an accurate predictive model that can assist consumers and industry stakeholders in estimating car prices effectively.

This project involves cleaning and preprocessing an automobile dataset, handling missing values, converting data types, normalizing features, and creating new columns. It also includes exploratory analysis with visualizations, such as a histogram of horsepower, to understand key patterns in the data.

This Python-based case study analyzes Austo Automobile business data to identify sales trends, customer demand patterns, and operational inefficiencies. Using data cleaning, exploratory analysis, and visualization, the project delivers actionable insights to support inventory planning, revenue growth, and data-driven strategic decision-making.

In this project I worked on an automobile industry dataset. I tried to find out the characteristics which have the most impact on the car price. I've done basic data wrangling and statistical relationship analysis.

Brand Analysis using Twitter data was my semester group project, which compares popularity and likeability between two automobile brands. One is Tata Safari and other is Mahinndra XUV 700. The project uses Machine learning topics such as Random Forest, Naive Bayes, Support Vector Machine and Logistic Regression.

This project provides a detailed analysis on the sales of vehicles from a top automobile company in the United States. The data used for this project was sourced from Kaggle, with major transformations carried out to derive key insights from the sales of over 400,000 units of vehicles.

This project is concerned with the automatic detection and analysis of work zones (construction zones) in naturalistic roadway images. An underlying motivation is to identify locations that may pose challenges to advanced driver-assistance systems or autonomous vehicle navigation systems. We first present an in-depth characterization of work zone scenes from a custom dataset collected from more than a million miles of naturalistic driving data. Then we describe two ML algorithms based on the ResNet and U-Net architectures. The first approach works in an image classification framework that classifies an image as a work zone scene or non-work zone scene. The second algorithm was developed to identify individual components representing evidence of a work zone (signs, barriers, machines, etc.). These systems achieved an F{0.5} score of 0.951 for the classification task and an F1 score of 0.611 for the segmentation task. We further demonstrate the viability of our proposed models through salience map analysis and ablation studies. To our knowledge, this is the first study to consider the detection of work zones in large-scale naturalistic data. The systems demonstrate potential for real-time detection of construction zones using forward-looking cameras mounted on automobiles.

In this project , we will perform various statistical methods and data visualization techniques to study relationships between vehicle characteristics such as price, engine size, fuel consumption and acceleration.

This is a 3 part python project run on Jupyter Notebook on an automobile dataset which includes data cleaning, exploratory analysis, descriptive statistics & predictive model development and evaluation.