{"repo":"fraisasghar/Machine-Learning-Classification-using-Python","free":true,"listed":false,"github":"https://github.com/fraisasghar/Machine-Learning-Classification-using-Python","clone":"git clone https://github.com/fraisasghar/Machine-Learning-Classification-using-Python.git","description":"This project applies machine learning techniques in Python to analyze data, visualize feature relationships, and perform classification using models such as Decision Tree and Support Vector Machine. Model performance is evaluated through confusion matrices and graphical analysis.","language":"Python","stars":119,"topics":["data-visualization","machine-learning","matplotlib","numpy","pandas","python","scikit-learn"],"license":"MIT","category":"machine-learning","readme_excerpt":"Introduction This project implements machine learning classification techniques using Python to analyze and interpret data patterns. The primary focus is on applying Decision Tree algorithms to classify data based on key features, with comprehensive evaluation through various performance metrics and visualization tools. The project demonstrates end-to-end machine learning workflow including data preprocessing, exploratory data analysis, model training, hyperparameter tuning, and performance evaluation using confusion matrices and classification reports. Objectives 1. Data Processing : Clean, preprocess, and prepare datasets for machine learning algorithms 2. Exploratory Data Analysis : Visualize data distributions and identify patterns 3. Model Implementation : Build and train Decision Tree classification models 4. Performance Optimization : Apply hyperparameter tuning using GridSearchCV 5. Evaluation : Assess model performance through various metrics and visualizations 6. Visualization : Create insightful plots for data and model analysis Dataset Overview The project utilizes a dataset containing: - Stock Price : Numerical values representing stock prices - Trading Volume : Numerical values representing trading volumes - Stock Name : Categorical labels for different stocks (AAL, AAPL, AAP, ABBV, ABC, ABT, ACN) Dataset Characteristics : - Total samples: 1,833 entries - Features: 2 numerical features (Stock Price, Trading Volume) - Target: 7 classes (stock names) - Data types:","default_branch":null,"files":null,"tree":[],"storefront":"/r/fraisasghar","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/fraisasghar/Machine-Learning-Classification-using-Python/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}