| 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 |