Machinelearning_projects

1. Handwritten Digit Recognition with MNIST Dataset

   • Description: Created a machine learning model to recognize handwritten digits using the MNIST dataset. Achieved an accuracy of over 95% by implementing a convolutional neural network (CNN).

   • Skills Used: Python, TensorFlow, Keras, Convolutional Neural Networks (CNN), Image Classification, Data Preprocessing.

2. Iris Flower Classification

   • Description: Developed a classification model to identify different species of iris flowers based on their features. Utilized a variety of machine learning algorithms, including decision trees and support vector machines (SVM), achieving high accuracy.

   • Skills Used: Python, Scikit-Learn, Machine Learning, Classification Algorithms, Data Preprocessing.

3. Sentiment Analysis with Natural Language Processing (NLP)

   • Description: Conducted sentiment analysis on a large text dataset to determine public opinion on a specific topic. Employed NLP techniques, including tokenization, word embeddings, and deep learning with LSTM networks, to predict sentiment.

   • Skills Used: Python, Natural Language Processing (NLP), Tokenization, Word Embeddings, LSTM Networks, Sentiment Analysis.

4. Linear Regression with Pyspark

   • Description: A simple Linear Regression project using Databricks with single cluster to predict total bill value against different independent features.

   • Skills Used: Pyspark, Databricks, Data Preprocessing, Linear Regression