Machine Learning and Deep learning Notebooks

Notebook Description	Link	Notes
Iris Flower Classification	Iris_flower_classification.ipynb	Build a neural network model using Keras & Tensorflow. Evaluated the model using scikit learn's k-fold cross validation.
Recognizing CIFAR-10 images (Part I - Simple model)	Recognizing-CIFAR-10-images-Simple-Model.ipynb	Build a simple Convolutional Neural Network(CNN) model to classify CIFAR-10 image dataset with Keras deep learning library achieving classification accuracy of 67.1%.
Recognizing CIFAR-10 images (Part II - Improved model)	Recognizing-CIFAR-10-images-Simple-Model.ipynb	Build an improved CNN model by adding more layers with Keras deep learning library achieving classification accuracy of 78.65%.
Recognizing CIFAR-10 images (Part III - Data Augmentation)	Recognizing-CIFAR-10-images-Improved-Model-Data-Augmentation.ipynb	Build an improved CNN model by data augmentation with Keras deep learning library achieving classification accuracy of 80.73%.
Traffic Sign Recognition using Deep Learning	Traffic-Sign-Recognition.ipynb	Build a deep learning model to detect traffic signs using the German Traffic Sign Recognition Benchmark(GTSRB) dataset achieving an accuracy of 98.4%.
Movie Recommendation Engine	Movie_Recommendation_Engine.ipynb	Build a movie recommendation engine using k-nearest neighbour algorithm implemented from scratch.
Linear Regression	Linear_Regression.ipynb	Build a simple linear regression model to predict profit of food truck based on population and profit of different cities.
Multivariate Linear Regression	Multivariate_Linear_Regression.ipynb	Build a simple multivariate linear regression model to predict the price of a house based on the size of the house in square feet and number of bedrooms in the house.
Sentiment Analysis of Movie Reviews	Sentiment_Analysis.ipynb	Experiment to analyze sentiment according to their movie reviews.
Wine quality prediction	Predicting_wine_quality.ipynb	Experiment to predict wine quality with feature selection (In progress).
Unsupervised Learning	unsupervised_learning-Part_1.ipynb	Hands-on with Unsupervised learning.
Autoencoders using Fashion MNIST	Autoencoder_Fashion_MNIST.ipynb	Building an autoencoder as a classifier using Fashion MNIST dataset.
Logistic Regression	Logistic_Regression.ipynb	Build a logistic regression model from scratch - Redoing it
Fuzzy string matching	fuzzywuzzy.ipynb	To study how to compare strings and determine how similar they are in Python.
Spam email classification	spam_email_classification.ipynb	Build a spam detection classification model using an email dataset.
Customer churn prediction	customer_churn_prediction.ipynb	To predict if customers churn i.e. unsubscribed or cancelled their service.- In Progress
Predicting Credit Card Approvals	predicting_credit_card_approvals.ipynb	To predict the approval or rejection of a credit card application