Pinned Repositories
AruneshTamboli
Credit-Card-Default-Prediction
Problem Description This project is aimed at predicting the case of customers default payments in Taiwan. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. We can use the K-S chart to evaluate which customers will default on their credit card payments
EDA-Hotel-Booking-Analysis
Exploring and analyzing the data to discover important factors that govern the bookings.
Face-Emotion-Recognition
1.1 Project Introduction The Indian education landscape has been undergoing rapid changes for the past 10 years owing to the advancement of web-based learning services, specifically, eLearning platforms. Global E-learning is estimated to witness an 8X over the next 5 years to reach USD 2B in 2021. India is expected to grow with a CAGR of 44% crossing the 10M users mark in 2021. Although the market is growing on a rapid scale, there are major challenges associated with digital learning when compared with brick and mortar classrooms. One of many challenges is how to ensure quality learning for students. Digital platforms might overpower physical classrooms in terms of content quality but when it comes to understanding whether students are able to grasp the content in a live class scenario is yet an open-end challenge. In a physical classroom during a lecturing teacher can see the faces and assess the emotion of the class and tune their lecture accordingly, whether he is going fast or slow. He can identify students who need special attention. Digital classrooms are conducted via video telephony software program (exZoom) where it’s not possible for medium scale class (25-50) to see all students and access the mood. Because of this drawback, students are not focusing on content due to lack of surveillance. While digital platforms have limitations in terms of physical surveillance but it comes with the power of data and machines which can work for you. It provides data in the form of video, audio, and texts which can be analysed using deep learning algorithms. Deep learning backed system not only solves the surveillance issue, but it also removes the human bias from the system, and all information is no longer in the teacher’s brain rather translated in numbers that can be analysed and tracked. 1.2 Problem Statements We will solve the above-mentioned challenge by applying deep learning algorithms to live video data. The solution to this problem is by recognizing facial emotions. 1.2.1 Face Emotion Recognition This is a few shot learning live face emotion detection system. The model should be able to real-time identify the emotions of students in a live class.
Miscellaneous-Projects
Online-Retail-Customer-Segmentation
Problem Description :In this project, your task is to identify major customer segments on a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.
Principal-Component-Analysis-on-The-Boston-Housing-Dataset
Introduction The purpose of this post is to explain Principal Component Analysis in a detailed and simplified way. I’ll cover the working of PCA step by step, so everyone can understand it and make use of this technique. What is PCA? Principal component analysis is fundamentally a dimensionality reduction algorithm, but it can also be useful as a tool for visualization, noise filtering, feature extraction, and engineering, and much more. PCA is the process of computing the principal components and using them to perform a change of basis on the data, sometimes using only the first few principal components and ignoring the rest.
Sales-Prediction-Predicting-sales-of-a-major-store-chain-Rossmann
Problem Description Rossmann operates over 3,000 drug stores in 7 European countries. Currently, Rossmann store managers are tasked with predicting their daily sales for up to six weeks in advance. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. With thousands of individual managers predicting sales based on their unique circumstances, the accuracy of results can be quite varied. You are provided with historical sales data for 1,115 Rossmann stores. The task is to forecast the "Sales" column for the test set. Note that some stores in the dataset were temporarily closed for refurbishment.
Seol-Bike-Sharing-Demand-Prediction
Problem Description Currently Rental bikes are introduced in many urban cities for the enhancement of mobility comfort. It is important to make the rental bike available and accessible to the public at the right time as it lessens the waiting time. Eventually, providing the city with a stable supply of rental bikes becomes a major concern. The crucial part is the prediction of bike count required at each hour for the stable supply of rental bikes.
Using-Python-to-automate-Google-Trends-Data
Google Trends is a fantastic tool for capturing Google users' search trends. I recently discovered pytrends and am still learning how to use the API, but I thought I'd share some of my findings. According to what I've learned, using Google Trends is simple, and automating and further analysing data with pytrends can truly optimise your strategies.
AruneshTamboli's Repositories
AruneshTamboli/Face-Emotion-Recognition
1.1 Project Introduction The Indian education landscape has been undergoing rapid changes for the past 10 years owing to the advancement of web-based learning services, specifically, eLearning platforms. Global E-learning is estimated to witness an 8X over the next 5 years to reach USD 2B in 2021. India is expected to grow with a CAGR of 44% crossing the 10M users mark in 2021. Although the market is growing on a rapid scale, there are major challenges associated with digital learning when compared with brick and mortar classrooms. One of many challenges is how to ensure quality learning for students. Digital platforms might overpower physical classrooms in terms of content quality but when it comes to understanding whether students are able to grasp the content in a live class scenario is yet an open-end challenge. In a physical classroom during a lecturing teacher can see the faces and assess the emotion of the class and tune their lecture accordingly, whether he is going fast or slow. He can identify students who need special attention. Digital classrooms are conducted via video telephony software program (exZoom) where it’s not possible for medium scale class (25-50) to see all students and access the mood. Because of this drawback, students are not focusing on content due to lack of surveillance. While digital platforms have limitations in terms of physical surveillance but it comes with the power of data and machines which can work for you. It provides data in the form of video, audio, and texts which can be analysed using deep learning algorithms. Deep learning backed system not only solves the surveillance issue, but it also removes the human bias from the system, and all information is no longer in the teacher’s brain rather translated in numbers that can be analysed and tracked. 1.2 Problem Statements We will solve the above-mentioned challenge by applying deep learning algorithms to live video data. The solution to this problem is by recognizing facial emotions. 1.2.1 Face Emotion Recognition This is a few shot learning live face emotion detection system. The model should be able to real-time identify the emotions of students in a live class.
AruneshTamboli/Using-Python-to-automate-Google-Trends-Data
Google Trends is a fantastic tool for capturing Google users' search trends. I recently discovered pytrends and am still learning how to use the API, but I thought I'd share some of my findings. According to what I've learned, using Google Trends is simple, and automating and further analysing data with pytrends can truly optimise your strategies.
AruneshTamboli/Credit-Card-Default-Prediction
Problem Description This project is aimed at predicting the case of customers default payments in Taiwan. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. We can use the K-S chart to evaluate which customers will default on their credit card payments
AruneshTamboli/EDA-Hotel-Booking-Analysis
Exploring and analyzing the data to discover important factors that govern the bookings.
AruneshTamboli/Miscellaneous-Projects
AruneshTamboli/Online-Retail-Customer-Segmentation
Problem Description :In this project, your task is to identify major customer segments on a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.
AruneshTamboli/Sales-Prediction-Predicting-sales-of-a-major-store-chain-Rossmann
Problem Description Rossmann operates over 3,000 drug stores in 7 European countries. Currently, Rossmann store managers are tasked with predicting their daily sales for up to six weeks in advance. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. With thousands of individual managers predicting sales based on their unique circumstances, the accuracy of results can be quite varied. You are provided with historical sales data for 1,115 Rossmann stores. The task is to forecast the "Sales" column for the test set. Note that some stores in the dataset were temporarily closed for refurbishment.
AruneshTamboli/Seol-Bike-Sharing-Demand-Prediction
Problem Description Currently Rental bikes are introduced in many urban cities for the enhancement of mobility comfort. It is important to make the rental bike available and accessible to the public at the right time as it lessens the waiting time. Eventually, providing the city with a stable supply of rental bikes becomes a major concern. The crucial part is the prediction of bike count required at each hour for the stable supply of rental bikes.
AruneshTamboli/AruneshTamboli
AruneshTamboli/Principal-Component-Analysis-on-The-Boston-Housing-Dataset
Introduction The purpose of this post is to explain Principal Component Analysis in a detailed and simplified way. I’ll cover the working of PCA step by step, so everyone can understand it and make use of this technique. What is PCA? Principal component analysis is fundamentally a dimensionality reduction algorithm, but it can also be useful as a tool for visualization, noise filtering, feature extraction, and engineering, and much more. PCA is the process of computing the principal components and using them to perform a change of basis on the data, sometimes using only the first few principal components and ignoring the rest.
AruneshTamboli/Pyspark-With-Python
AruneshTamboli/Python-Practice-Problems-with-logic-and-Explanation
AruneshTamboli/Python-Projects