This repository contains a collection of machine learning projects completed during my internship at Sparks Foundation. The projects cover a range of topics including data anlysis, data visualization and predictive modeling. Each project includes a detailed README file with instructions on how to run the code and explanations of the techniques used. These projects were completed using Python and various libraries such as Scikit-learn, Pandas, NumPy, Seaborn and Matplotlib.
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Project 1: Predicting Students Percentage based on the No. of Hours they study using Simple Linear Regression
Techniques used: Data Analysis, Data Visualizing, Data Splitting, Model Creation and Model Prediction
Results achieved: I obtained accuracy of 92% and predicted the model using new different data's -
Project 2: Clustering Different Species of iris flower using K-Means
Techniques used: Data Analysis, Data Visualization, Data Splitting, K-Means Clustering, Clusters Visualization
Dataset used: Iris Dataset
Results achieved: I have obtained 3 cluster for 3 different species using the The Elbow Method. -
Project 3: Classifying the different Species using of iris Flower using Decision Tree
Techniques used: Data Analysis, Data Visualization, Data Splitting, Model Creation, Tree Visualization
Dataset used: Iris Dataset
Results achieved: I achieved an accuracy of 100%. Model has done all the predictions correctly.