The deployed web-app is live now - Give Web-App a Try Developed an app that forecasts flight ticket prices with 94.88 % accuracy Preprocessed the data to a specific data form which is suitable for machine learning model Applied Dummy Encoding to Categorical Features such as Airline, Source and Destination Generated Feature Importance using Random Forest Algorithm Built a web-app using Streamlit and deployed it to the Heroku: A Cloud Application Platform – Feature Importance Plot – Prediction vs Actual Link to Dataset

Checked Target Incidence using a Binary classifier Model which provides the output in ‘0 - the donor will not give blood’ and ‘1 - the donor will give blood’. Result is [‘0’ - 0.762] & [‘1’ - 0.238] Zeroed into LogisticRegression model using TPOT, a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming, giving us the AUC score of 0.7850 Improved the AUC score by 0.

Loaded and Viewed the confidential dataset, as the contributor of the dataset has anonymized the feature names Read this blog, to better understand the anonymized features Handled all missing values, as they affect the performance of machine learning model if they go unchanged Preprocessed the data into Three main tasks: Converted the non-numeric data into numeric, Splitting the data into train and test sets, and Scaled the feature values to a uniform range Fitted a Logistic Regression model (a generalized linear model), and Evaluated the model on the test set with respect to classification accuracy, and summarized the performance of a classification algorithm using Confusion matrix Performed GridSearchCV by defining the grid of values to two hyperparameters ‘tol’ and ‘max_iter’ to improve the model’s ability to predict credit card approvals Summarized the best achieved model score of 85% and the respective best parameters Link to GitHub Repository

Developed a tool estimating the salary for the position of data science job with (MAE) ~ $13k posted on glassdoor.com in the United States Scraped 1000 Jobs lists from Glassdoor using python and selenium Performed Exploratory Data Analysis to uncover the underlying important structure of the Glassdoor job dataset Engineered features from the text of each job description to quantify the value companies put on Python, SQL, R, Spark, AWS and Excel Optimized Linear, Lasso, and Random Forest Regressors using GridsearchCV to reach the best model Created a client facing API using flask Link to GitHub Repository

Performed Exploratory Data Analysis to find correlation and distribution of various parameters Applied Linear Regression Machine Learning Model to predict the target variable ‘Chance of Admit’ Identified most crucial parameter for ‘High Chance of Admit’ by applying Feature Importance using Random Forest Regressor Link to GitHub Repository

Cleaned the data and conducted Exploratory Data Analysis Transformed hashable and comparable non-numerical labels to numerical labels using LabelEncoder Applied Polynomial and Linear Regression Machine Learning Models to predict the target variable ‘Insurance Cost’ accurately Link to GitHub Repository