KRISHNA CHAITANYA YARLAGADDA

This repository focuses on building several Regression Models-Linear Regression, XGBoost Regressor, Ridge Regression, Lasso Regression, Polynomial Regression that predicts the continuous outcome (House Prices) along with several Data Preparation Techniques (Transformations/Scaling, Imputation, Filtering of Outliers, Handling of correlated features, One Hot Encoding)

This repository focuses on building Time Series Model (Recurrent Neural Network- LSTM) to predict the stock price of the Apple.Long Short-Term Memory (LSTM) networks are a type of recurrent neural network capable of learning order dependence in sequence prediction problems that involves time series related events

This repository focuses on building Association rules (Market Basket Analysis) using Support, Confidence, Lift that gives insights on which products go together in the cart of customer's shopping

This repository contains code to build various versions of Decision Tree Models (Gini, Entropy) for the Titanic Passenger Survival prediction from Kaggle Competitions. Pruning is also performed to reduce the Overfitting

This repository contains code for PCA (Principal Component Analysis with fixed number of components) that reduces the number of dimensions, PCA with Scree plot (finding number of optimal components that explains maximum variance) in Fraud data set from Kaggle competition that contains more than 500 variables.

This repository focuses on building K-Means Clustering (Unsupervised Learning algorithm) that builds the effective number of cluster grouping/segmentation based on Elbow method.

This repository focuses on building several Classification Machine Learning algorithms from scratch following the standard approach of using Data Exploration/Data Preparation techniques. Several algorithms (Logistic, XGBoost, KNN, SVC, Naive Bayes) were trained and used to predict the unseen data. All the algorithms were built as part of Analytics Vidhya Competition

This repository focuses on building several versions of Deep Learning Neural Network Models (Sequential Model, Model with increase in hidden layers, Model with Regularization to avoid overfitting) with Keras that uses TensorFlow Back end.

Sentiment Mining (Unstructured data)- This repository focuses on Creating a Word Cloud (with most frequent/significant words) and Created list of top words by product, K-Means and PCA plot for the reviews based on category of topics as pulled by the textual review analysis of Amazon Customer Reviews on Electronic Products

This is my first repository created with Basic Python Operations and Data Analysis using Pandas. All the basic SQL Operations/Methods that we regularly use to retrieve/merge data is translated to Python Programming

This repository focuses on code to import the datasets from S3 Bucket into SageMaker and use them to build/train Machine Learning models with in AWS SageMaker

This repository contains code to build 14 Classification Machine Learning Models to predict the decision of Loan Approval Process with PyCaret. Models are compared against the statistics (Accuracy, F1-Score), best model was picked, tuned, saved/loaded for model deployment and used to predict the observations on unseen data. The final file with predictions on unseen data was submitted to Analytics Vidhya Hackathon with model accuracy of 88%

This repository contains code to build 21 Regression Machine Learning Models to predict the house price in Python using PyCaret. Models are compared against the statistics (RMSE), best model was picked, tuned, saved/loaded for model deployment and used to predict the observations on unseen data. The final file with predictions on unseen data was submitted to Kaggle Competition placing me in Top 20%

This repository focuses on using simple lines of code with several Python packages(pandas-profiling, sweetviz, dtales) that does the extensive detailed Exploratory Data Analysis by producing several uni-variate statistics

This ChatBot was implemented using NLTK (Natural Language ToolKit) with Python. ChatBot uses rule based retrieving of providing definitions to Machine Learning and Statistics terms based on user request. For simplicity, only few terms were added to the ChatBot response file

This repository focuses on Deploying the Machine Learning model on Web using Streamline (via localhost) and Heroku (via Web). Web model deployed using Heroku can be used both for Online Predictions (that takes in user input values for the variables), Batch Predictions (that takes in Excel file of unseen data) and predicts the house price

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