This repository contains a collection of Python projects focused on Data Science Analytics and Machine Learning. The projects leverage powerful libraries such as NumPy and Pandas to perform various data manipulation, analysis, and visualization tasks. Each project is designed to demonstrate practical applications of these libraries in solving real-world data problems. Includes implementations of regression, classification, clustering, neural networks, and more.
Techniques for handling missing values, data transformation, and preparation.
Methods to explore datasets, understand data distributions, and identify patterns.
Efficient numerical computations and array manipulations using NumPy.
Creating insightful visualizations using libraries like Matplotlib and Seaborn integrated with Pandas.
Analyzing and forecasting time series data using Pandas and other related libraries.
Advanced techniques in data merging, reshaping, and grouping with Pandas.
Regression Analysis: Implementations of linear and polynomial regression to predict continuous outcome.
Classification Models: Projects featuring logistic regression, Decision Trees, Random Forests, and Support Vector Machines for categorical prediction tasks.
Clustering Techniques: K-means clustering, Hierarchical clustering, and DBSCAN for grouping similar data points.
Neural Networks: Introduction to Neural networks using frameworks like TensorFlow and Keras, including simple feedforward networks and Convolutional Neural Networks (CNNs).
Model Evaluation and Tuning: Techniques for model evaluation, including cross-validation, confusion matrix, and ROC curves. Hyperparameter tuning using GridSearchCV and RandomizedSearchCV.
Natural Language Processing (NLP): Basic NLP tasks such as text preprocessing, sentiment analysis, and topic modeling.
Dimensionality Reduction: PCA, t-SNE, and other techniques to reduce the dimensionality of data while preserving important features.