Tech-with-Vidhya's repositories
bank_customers_churn_prediction_exploring_7_different_classification_algorithms
This project deals with the classification of the bank customers on whether a customer will leave the bank (i.e.; churn) or not, by applying the below steps of a Data Science Project Life-Cycle 1. Data Exploration, Analysis and Visualisations 2. Data Pre-processing 3. Data Preparation for the Modelling 4. Model Training 5. Model Validation 6. Optimized Model Selection based on Various Performance Metrics 7. Deploying the Best Optimized Model into Unseen Test Data 8. Evaluating the Optimized Model’s Performance Metrics The business case of determining the churn status of bank customers are explored, trained and validated on 7 different classification algorithms/models as listed below and the best optimized model is selected based on the accuracy metrics. 1. Decision Tree Classifier - CART (Classification and Regression Tree) Algorithm 2. Decision Tree Classifier - IDE (Iterative Dichotomiser) Algorithm 3. Ensemble Random Forest Classifier Algorithm 4. Ensemble Adaptive Boosting Classifier Algorithm 5. Ensemble Hist Gradient Boosting Classifier Algorithm 6. Ensemble Extreme Gradient Boosting (XGBoost) Classifier Algorithm 7. Support Vector Machine (SVM) Classifier Algorithm
financial_consumer_complaints_tableau_dashboard
This project includes the data analysis related to financial consumer complaints using Tableau Desktop and results are visualized in the form of a Tableau Dashboard.
capital_markets_stocks_trade_transactions_tableau_dashboard
This project includes the data analysis related to the capital stock market trade transactions data using Tableau Desktop and results are visualized in the form of a Dynamic Tableau Dashboard.
spark_fund_investment_EDA_and_data_cleaning_with_profiling_report
This project deals with the Exploratory Data Analysis and the Data Cleaning of the various Companies Data for Spark Fund Investment, along with the Profiling Report.
audio-digits-classification-using-MFCC-and-convolutional-neural-network
This project is delivered as part of my Masters in Big Data Science (MSc BDS) Program Module named “Machine Learning” in Queen Mary University of London (QMUL), London, United Kingdom. The project cover the basic solution and the Advanced Solution as given below based on Audio Feature Extraction Method named "Mel-frequency cepstral coefficients (MFCC)" and Deep Learning Convolutional Neural Network (CNN). Basic Solution: Includes designing, building, training, validation and testing a model created to recognise numerals from 0 to 9 in the audio files. Advanced Solution: Includes implementing the solution to predict the numeral based on a new audio test file. This model's solution can be applied to a Banking Application/Product and can be used for predicting a 4-digit passcode said by an authorised customer during on-call verification as part of login process to Internet Banking Account. NOTE: Due to the data privacy and the data protection policy to be adhered by the students; the datasets and the solution related code are not exposed and updated in the GitHub public profile; in order to be compliant with the Queen Mary University of London (QMUL) policies.
bank-customers-churn-prediction-using-decision-tree-classifier-cart-algorithm
This project deals with the classification of the bank customers on whether a customer will leave the bank (i.e.; churn) or not, using Decision Tree CART (Classification And Regression Tree) Algorithm.
bank-customers-churn-prediction-using-decision-tree-classifier-ide-algorithm
This project deals with the classification of the bank customers on whether a customer will leave the bank (i.e.; churn) or not, using Decision Tree ID3 (Iterative Dichotomiser) Algorithm.
bank-customers-churn-prediction-using-ensemble-adaptive-boosting-classifier-algorithm
This project deals with the classification of the bank customers on whether a customer will leave the bank (i.e.; churn) or not, using Ensemble Adaptive Boosting Classifier Algorithm.
bank-customers-churn-prediction-using-ensemble-extreme-gradient-boosting-classifier-algorithm
This project deals with the classification of the bank customers on whether a customer will leave the bank (i.e.; churn) or not, using Ensemble Extreme Gradient Boosting (XGBoost) Classifier Algorithm.
bank-customers-churn-prediction-using-ensemble-hist-gradient-boosting-classifier-algorithm
This project deals with the classification of the bank customers on whether a customer will leave the bank (i.e.; churn) or not, using Ensemble Hist Gradient Boosting Classifier Algorithm.
bank-customers-churn-prediction-using-ensemble-random-forest-classifier-algorithm
This project deals with the classification of the bank customers on whether a customer will leave the bank (i.e.; churn) or not, using Ensemble Random Forest Classifier Algorithm.
bank-customers-churn-prediction-using-support-vector-machine-classifier-algorithm
This project deals with the classification of the bank customers on whether a customer will leave the bank (i.e.; churn) or not, using Support Vector Machine (SVM) Classifier Algorithm.
Colored-Text-in-Python
This project will display texts and strings in coloured format with various foreground colours, background colurs and styles.
Display-Right-Angled-Triangle
This project will display a right-angled triangle as an output in various symbols namely:"*", "|" and ".".
image-super-resolution-using-deep-learning-CNN-and-PSNR
This project is delivered as part of my Masters in Big Data Science (MSc BDS) Program internal training for the module named “Deep Learning and Computer Vision” in Queen Mary University of London (QMUL), London, United Kingdom. This project aims to obtain practical knowledge and hands-on understanding of the concepts of image super-resolution, deep learning using convolutional neural networks (CNN) and peak signal-to-noise ratio (PSNR). The project addresses 3 different problem statements and use cases; with the solutions implemented using Python and its module named PyTorch.
ML-Predicting-Company-Profit-Linear-Regression-Model-without-scikitlearn
This repo includes a Simple Linear Regression Machine Learning Model of predicting a company's profit based on the "R&D Spend" amount; with a dataset that includes 1000 companies with labelled data.
ML-Salary-Prediction-Linear-Regression-Model-Project
This repo includes a Linear Regression Machine Learning Model of predicting salary based on the years of experience; with a dataset that includes labelled data.
NLP-deception-detection-classification-tfidf-amazon-reviews
This project is delivered as part of my Masters in Big Data Science (MSc BDS) Program for the module named “Natural Language Processing” in Queen Mary University of London (QMUL), London, United Kingdom. This project covers the implementation of the classification for Deception Detection of the Amazon text reviews private dataset using TF-IDF (Term Frequency–Inverse Document Frequency) **NOTE:** Due to the data privacy and the data protection policy to be adhered by the students; the datasets and the solution related code are not exposed and updated in the GitHub public profile; in order to be compliant with the Queen Mary University of London (QMUL) policies.
Python-Data-Analysis-Pokemon-Pandas
This project includes the data analysis of pokemon dataset using python library "pandas".
Python-Data-Visualisations-Matplotlib
This project includes data visualisations using python library "Matplotlib" with "pyplot".
R-programming-statistical-data-analysis-and-visualisations
This project is delivered as part of my Masters in Big Data Science (MSc BDS) Program for the module named “Applied Statistics” in Queen Mary University of London (QMUL), London, United Kingdom. The project covered the descriptive statistical analysis, data analysis and visualisations in R programming for the “njgolf” dataset. **NOTE:** Due to the data privacy and the data protection policy to be adhered by the students; the datasets and the solution related code are not exposed and updated in the GitHub public profile; in order to be compliant with the Queen Mary University of London (QMUL) policies.
R-programming-statistical-modelling-of-linear-regression
This project is delivered as part of my Masters in Big Data Science (MSc BDS) Program for the module named “Applied Statistics” in Queen Mary University of London (QMUL), London, United Kingdom. The project covered the statistical modelling of linear regression machine learning algorithm implemented in R programming for the “njgolf” dataset. **NOTE:** Due to the data privacy and the data protection policy to be adhered by the students; the datasets and the solution related code are not exposed and updated in the GitHub public profile; in order to be compliant with the Queen Mary University of London (QMUL) policies.
stock_market_status_summary_tableau_dashboard
This project includes the data analysis related to stock market data using Tableau Desktop and results are visualized in the form of a Tableau Dashboard.
Sudoku-Puzzle-Game-Solver
This project will solve a given Sudoko Puzzle Game by itself and displays the unsolved puzzle first followed by displaying the solved puzzle as outputs in the console.
twitter-time-series-data-linear-and-quadratic-regression-in-R-programming
This project is delivered as part of my Masters in Big Data Science (MSc BDS) Program for the module named “Applied Statistics” in Queen Mary University of London (QMUL), London, United Kingdom. The project covered the statistical modelling of the linear regression and quadratic regression machine learning algorithms implemented in R programming for the “twitter time series” dataset. **NOTE:** Due to the data privacy and the data protection policy to be adhered by the students; the datasets and the solution related code are not exposed and updated in the GitHub public profile; in order to be compliant with the Queen Mary University of London (QMUL) policies.
twitter-time-series-data-various-statistical-distributions-analysis-R-programming
This project is delivered as part of my Masters in Big Data Science (MSc BDS) Program for the module named “Applied Statistics” in Queen Mary University of London (QMUL), London, United Kingdom. The project covered the statistical analysis of the various distributions namely normal, logistic, poisson, Weibull and gamma in R programming for the “twitter time series” dataset. **NOTE:** Due to the data privacy and the data protection policy to be adhered by the students; the datasets and the solution related code are not exposed and updated in the GitHub public profile; in order to be compliant with the Queen Mary University of London (QMUL) policies.