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https://github.com/saksham-jain177/data-analysis

A collection of data analysis and machine learning projects across various datasets. Explore predictive modeling, data visualization, and insights from real-world data. Projects include sales predictions, disease detection, customer segmentation, and more.
https://github.com/saksham-jain177/data-analysis

api data data-analysis data-cleaning data-science data-visualization datamodeling dataset datasets exploratory-data-analysis python python3 web-scraping youtube-api

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A collection of data analysis and machine learning projects across various datasets. Explore predictive modeling, data visualization, and insights from real-world data. Projects include sales predictions, disease detection, customer segmentation, and more.

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README 

        
# Data Analysis Projects



Welcome to my repository of Data Analysis projects! This repository contains a series of notebooks demonstrating different data analysis and machine learning tasks. Each project focuses on a unique dataset and problem statement, showcasing various analytical and predictive techniques.



## Table of Contents



1. [Swiggy Restaurants Data Analysis](#swiggy-restaurants-data-analysis)

2. [GeeksforGeeks Data Analysis](#geeksforgeeks-data-analysis)

3. [Cardekho Used Car Price Analysis](#cardekho-used-car-price-analysis)

4. [Sonar Mine Prediction](#sonar-mine-prediction)

5. [Big Mart Sales Prediction](#big-mart-sales-prediction)

6. [California House Price Prediction](#california-house-price-prediction)

7. [CarDekho Car Price EDA](#cardekho-car-price-eda)

8. [Credit Card Fraud Detection](#credit-card-fraud-detection)

9. [Customer Segmentation Using K-Means](#customer-segmentation-using-k-means)

10. [Fake News Prediction](#fake-news-prediction)

11. [Gold Price Prediction](#gold-price-prediction)

12. [Heart Disease Prediction](#heart-disease-prediction)

13. [House Prices: Advanced Regression Techniques](#house-prices-advanced-regression-techniques)

14. [Loan Eligibility Prediction](#loan-eligibility-prediction)

15. [Parkinson's Disease Detection](#parkinsons-disease-detection)

16. [Spam Mail Prediction](#spam-mail-prediction)

17. [Used Medical Insurance Prediction](#used-medical-insurance-prediction)



## Swiggy Restaurants Data Analysis



**Description:** This project involves analyzing restaurant data from the Swiggy food delivery platform. Key aspects include:



- **Data Collection:** Access data on restaurant names, cuisines, ratings, reviews, delivery times, and locations.

- **Data Cleansing and Preparation:** Clean and preprocess the data for analysis.

- **Restaurant Performance Analysis:** Calculate average ratings, review counts, and identify high-performing restaurants.

- **Cuisine and Menu Analysis:** Analyze cuisine distribution and popular menu items.



## GeeksforGeeks Data Analysis



**Description:** This project involves scraping and analyzing video data from the GeeksforGeeks YouTube channel.



- **Data Gathering:** Use YouTube Data API to fetch video details such as titles, views, upload dates, and lengths.

- **Data Processing and Analysis:** Calculate total views and lengths, identify popular topics, and analyze correlations.

- **Visualization:** Use libraries like matplotlib to create visualizations of trends and patterns.



## Cardekho Used Car Price Analysis



**Description:** Analyze the used car dataset from Cardekho to uncover insights about factors influencing car prices.



- **Data Gathering:** The dataset includes features like selling price, vehicle age, KM driven, engine size, fuel type, seller type, and transmission type.

- **Data Cleaning and Preprocessing:** Handle missing values, remove duplicates, standardize text columns, and remove outliers.

- **Exploratory Data Analysis (EDA):** Perform univariate, bivariate, and categorical analyses to identify key trends and insights.

- **Visualization:** Use libraries like matplotlib and seaborn to create distribution plots, scatter plots, and correlation heatmaps.

- **Insights and Findings:** Analyze the impact of various factors on car prices and provide recommendations based on the analysis.



## Sonar Mine Prediction



**Description:** Build a machine learning model to classify sonar signals as either mines (M) or rocks (R).



- **Data Gathering:** The dataset includes sonar readings for mines and rocks.

- **Data Cleaning and Preprocessing:** Verify and handle missing values and outliers.

- **Exploratory Data Analysis (EDA):** Analyze summary statistics and class distribution.

- **Model Building:** Create feature matrices, split data, and evaluate models such as Logistic Regression, SVC, Decision Tree, and Random Forest.

- **Model Comparison:** Compare models based on accuracy and performance metrics.

- **Insights and Findings:** Determine the best model for sonar signal classification based on accuracy.



## Big Mart Sales Prediction



**Description:** Predict sales for Big Mart using historical sales data.



- **Data Gathering:** Use sales data from Big Mart to create predictive models.

- **Data Cleaning and Preprocessing:** Handle missing values and preprocess data for modeling.

- **Model Building:** Build and evaluate regression models to predict sales.



## California House Price Prediction



**Description:** Predict house prices in California using historical data.



- **Data Gathering:** Use historical housing data from California.

- **Data Cleaning and Preprocessing:** Clean and preprocess data for analysis.

- **Model Building:** Develop regression models to predict house prices.



## CarDekho Car Price EDA



**Description:** Perform exploratory data analysis on CarDekho's car price dataset.



- **Data Gathering:** Analyze features such as car price, model, and mileage.

- **Exploratory Data Analysis (EDA):** Identify key trends and patterns in the dataset.



## Credit Card Fraud Detection



**Description:** Build a model to detect fraudulent credit card transactions.



- **Data Gathering:** Use historical credit card transaction data.

- **Model Building:** Develop and evaluate classification models to detect fraud.



## Customer Segmentation Using K-Means



**Description:** Segment customers into different groups using K-Means clustering.



- **Data Gathering:** Use customer data for clustering.

- **Model Building:** Apply K-Means clustering to segment customers.



## Fake News Prediction



**Description:** Predict whether a news article is fake or real.



- **Data Gathering:** Use a dataset of news articles.

- **Model Building:** Develop and evaluate classification models for fake news detection.



## Gold Price Prediction



**Description:** Predict gold prices using historical data.



- **Data Gathering:** Use historical gold price data.

- **Model Building:** Develop regression models to predict future gold prices.



## Heart Disease Prediction



**Description:** Predict the likelihood of heart disease based on patient data.



- **Data Gathering:** Use health data related to heart disease.

- **Model Building:** Develop classification models to predict heart disease risk.



## House Prices: Advanced Regression Techniques



**Description:** Use advanced regression techniques to predict house prices.



- **Data Gathering:** Use historical housing data.

- **Model Building:** Apply advanced regression techniques to improve predictions.



## Loan Eligibility Prediction



**Description:** Predict loan eligibility based on applicant data.



- **Data Gathering:** Use applicant data to determine loan eligibility.

- **Model Building:** Develop classification models to predict loan approval.



## Parkinson's Disease Detection



**Description:** Build a model to detect Parkinson's disease from patient data.



- **Data Gathering:** Use health data related to Parkinson's disease.

- **Model Building:** Develop and evaluate classification models for disease detection.



## Spam Mail Prediction



**Description:** Predict whether an email is spam or not.



- **Data Gathering:** Use email data to classify spam and non-spam emails.

- **Model Building:** Develop classification models to detect spam emails.



## Used Medical Insurance Prediction



**Description:** Predict the likelihood of medical insurance usage based on patient data.



- **Data Gathering:** Use patient data to predict insurance usage.

- **Model Building:** Develop classification models to predict medical insurance needs.



## License



This project is licensed under the MIT License.