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car_prediction: Repository for the car prediction project.
This project aims to predict car selling prices based on various features such as mileage, fuel type, and transmission. It utilizes machine learning techniques and regression models to provide accurate price predictions.
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sales_prediction: Repository for the sales prediction project.
This project focuses on predicting sales figures for a business based on historical data and various factors such as marketing spend, seasonality, and product features. It employs regression models and time series analysis to forecast sales accurately.
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spam: Repository for the spam classification project.
In this project, machine learning algorithms are used to classify emails as spam or non-spam. It involves preprocessing text data, feature extraction, and training classification models such as Naive Bayes or Support Vector Machines to detect spam emails effectively.
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unemployment: Repository for the unemployment analysis project.
This project analyzes unemployment data to understand trends and patterns in joblessness. It includes data visualization, statistical analysis, and interpretation of unemployment rates across different regions and time periods.
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iris.py: Python script for the infamous Iris dataset classification project.
The Iris dataset classification project is a classic machine learning task where the goal is to classify iris flowers into three species based on their petal and sepal dimensions. The infamous aspect refers to the dataset's popularity and its usage as a benchmark in the machine learning community.
Use the navigation to explore files and folders within the repository.
This documentation represents my interpretation of these projects, and I acknowledge that I still have many things to learn in the field of machine learning and data analysis.