Adil Khan Email & Phone Number

Remote

1) Worked with SQL and gained a more depth understanding of Database.2) Improved Math knowledge and statistic knowledge for Machine Learning.3) Worked on understanding Machine Learning (Projects).

Remote

1. Formulated framework that predicts and explains the drivers that affect the variation of mobile prices. 2. Performed Exploratory Data analysis to understand the relation between the dependent and independent variables. 3. Predicted price of Smartphones based on 21 different features. Random Forest Classifier, XGBoost, and Logistic Regression were implemented and performed Hyperparameter tuning for every model using BayesSearchCV . Metrics used - Precision, Recall, f1 scores, auc_roc score. 4. Gradient boosting gave the best result. A model based on gradient boosting can be utilized to increase user experience.

Remote

Classification Project On Credit Card Default Prediction.1) Developed binary classification model that can predict whether a customer would be able to pay credit card payment using algorithms like Logistic Regression, SVC, and XGBoost.2) Understanding data set and for better modeling algorithms like Classification Regression, KNN, XGBClassifier and Random Forest. So the default rate non-payable of credit card user could be Decreased.3) Performed Data Cleaning, Exploratory Data Analysis, Correlation Analysis and Feature Transformation for better understanding and improving data.4) After comparison of all model, XGBClassifier model has the best result with 67% Recall and 82% Accuracy on Test Data which is improvement in accuracy with 6%.

Remote

Regression Project On Seoul Bike Sharing Demand.1) Supervised Regression - based approach, and the ML model that used to predict the number of rental bikes required per hour. Rental bikes demand was modeled using the available independent variables.2) Develop Regression model which give good predict on demand of Bike Sharing Per Hour, with using Regression Algorithms such as Linear Regression, Lasso Regression, Ridge regression, Random Forest and many more.3) Performed Label encoding for categorical feature, Correlation Analysis, EDA for improving the data before the modelling.4) In all Regression model The Gradient Boosting Regressor accuracy of 89.2% which is improvement in prediction by 34%.

Remote

Exploratory Data Analysis Project On Hotel Booking.1) This EDA (Exploratory Data Analysis) project contained real world data record of hotel bookings of city and resort hotel containing details like bookings, cancellations, guest details and many more.2) Main aim of the project is to understand and visualize dataset from hotel and customer point of view.3) Number of customers is more in the summertime, 60% of customers choose City hotel and rest choose resort, and May is most profitable for Hotels.

Remote

1) Developed a good understanding of Python and learn and learn libraries like Numpy, Pandas and Matplotlib.2) Fundamentals of Excel.3) Fundamentals of Tableau.

Remote

Gained hands-on experience with various tools and libraries such as Python, SQL, Tableau, MS Excel, Scikit-learn, Pandas, Numpy, Matplotlib, Seaborn and Statistics to execute Real-World ML problems