Overview: Welcome to my machine learning repository featuring predictive modeling projects! In this collection, I've developed and implemented machine learning models to predict house pricing, TV pricing, and salary values. These projects showcase my proficiency in data analysis, feature engineering, and predictive modeling using Python and popular machine learning libraries.
Projects:
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House Pricing Prediction: Explore my analysis and prediction model for house pricing. Leveraging a dataset containing various features related to real estate, I employed regression techniques to predict house prices accurately. The Jupyter Notebook (House_Pricing_Prediction.ipynb) outlines the data preprocessing steps, model training, and evaluation.
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TV Pricing Prediction: Discover my project on predicting TV prices using machine learning. The dataset includes attributes such as screen size, brand, and other relevant features. The associated Jupyter Notebook (TV_Pricing_Prediction.ipynb) details the entire modeling process, from data exploration to making predictions.
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Salary Prediction: Dive into my salary prediction project, where I developed a model to estimate salaries based on various factors such as education, experience, and job role. The associated Jupyter Notebook (Salary_Prediction.ipynb) provides insights into the data analysis and model building process.
Key Features: Data Preprocessing: Each project includes thorough data preprocessing steps to clean and prepare the datasets for modeling.
Machine Learning Models: Utilized regression models, including Linear Regression and other ensemble techniques, to make accurate predictions.
Jupyter Notebooks: The code is presented in Jupyter Notebooks, making it easy to understand and replicate the analysis.
Visualizations: Visualized key insights and model performance using Matplotlib