Power-BI Training

Building a Photo Editor from scratch using Flask and Numpy

Numpy is a versatile package to do data operations on matrices, numbers and number operations. With this project we will learn doing number operations on Images and apply filters, cropping, flipping and resizing using Numpy.
Industry: Media and Photography
Tools: Python, Numpy, Image Processing, Matplotlib

Exploratory Data Analysis on Retail Shop Sales data.

Performing exploratory data analysis to find patterns in data which will determine the approach to take in Machine Learning. Exploratory data analysis will help identifying features which can be used or discarded in the Machine Learning approach.
Industry: Retail
Tools: Exploratory Data Analysis, Data Preprocessing, Scaling Techniques, Missing Value Imputations.

Fruit type prediction using K Nearest Neighbors algorithm

K Nearest Neighbor algorithm is used to predict the type of fruit given its mass, height, width and color score. We will understand on how to use KNN algorithm and how to tune the hyperparameters such as n_neighbors and k value.
Industry: Food and Beverages
Tools: Supervised Machine Learning, K Nearest Neighbor Classifier, KNN Algorithm, Pandas, Numpy, Matplotlib

Predict Malignancy in Mammographic Masses using Decision Tree Classifiers

Mammographic masses is a public dataset from UCI machine learning repository which has information on mass shape, structure, density and age of a person. With this project we will use Decision Tree classifier to detect Malignancy (either Malignant or Benign) of the Mammographic Mass
Industry: Healthcare
Tools: Decision Tree Classifier, Sklearn, Pandas, Numpy, Graphviz, Matplotlib

Predict whether a candidate will be shortlisted in H1B Visa process using Random Forest Algorithm.

Every year there are close to 1 lakh 65 thousand applications for H1B Visa processing and only few get shortlisted in the process. A lot of information is used for scrutinizing candidates in the selection process. In this project we will use Random Forest algorithm on past five years of H1B processing data to identify if a H1B application will be selected or rejected in the application process.
Industry: US H1B Visa - USCIS
Tools: Random Forest, Pandas, Sklearn, Numpy, Seaborn, Matplotlib

Predict Breast Cancer using Support Vector Machine algorithm.

Wisconsin Breast Cancer dataset has 569 sample of Breast cancer observations determining Malignancy or Benign state of breast mass. Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. In this project we will use SVM algorithm to detect malignancy in the cell.
Industry: Healthcare
Tools: SVM, Numpy, Pandas, Sklearn

HR Analytics for Attrition Prediction using Logistic Regression

Employee Attrition is an important subject to gauge the satisfaction of the employee in a company. HR departments take various measures to arrest employee attrition. In this project we will use Logistic regression to predict who is the potential employee who is in a verge of leaving the company.
Industry: Human Resources
Tools: Logistic Regression, Sklearn, Numpy, Pandas, Matplotlib
Churn Analysis in Telecommunication:
A customer can be called as a “churner” when he/she discontinue their subscription in a company and move their business to a competitor. Prediction as well as prevention of customer churn brings a huge additional revenue source for every business.
Here, we use a telecom customer data set to classify the set of possible customers who are likely to churn
Industry: Telecommunications
Tools: SVM, K Nearest Neighbors Classifier, Random Forest, Logistic Regression, Pandas, Numpy, Sklearn, Seaborn, Matplotlib

Predicting Housing prices using Regression

Description: Predict the sales price for each house based on input features provided for the house.
Industry: Real Estate
Tools: Linear Regression, Multi Linear Regression, Sklearn, Pandas, Numpy, Matplotlib

Social Network Ads based Prediction

Description: Predicting if a user buys a specific product or not based on the ad populated on the Social Network. Data uses specific demographic information about the user to do the prediction.
Industry: Social Media
Tools: Hierarchical Clustering, Scipy, Sklearn, Pandas, Numpy, Seaborn

Retail Customer segmentation based on spending patterns

Description: Customer analysis plays a crucial role in determining the profitability of Retail companies. Segmentation of the customers based on their purchase patterns helps Retail companies to cluster their user base and serve them effectively.
Industry: Retail
Tools: K-Means, Clustering, Sklearn, Pandas, Numpy, Seaborn

Market Basket Analysis using Apriori Algorithm

Market Basket Analysis is a technique which identifies the strength of association between pairs of products purchased together and identify patterns of co-occurrence. A co-occurrence is when two or more things take place together.
The technique determines relationships of what products were purchased with which other product(s).
Industry: Retail
Tools: Apriori Algorithm, Numpy, Pandas

SMS Spam Detection using Natural Language Processing

In our day-to-day lives, we receive a large number of spam/junk messages either in the form of Text (SMS) or E-mails. It is important to filter these spam messages since they are not truthful or trustworthy.
In this case study, we apply various machine learning algorithms to categorize the messages depending on whether they are spam or not.
Industry: Social Media
Tools: NLP, Sklearn, Logistic Regression

Sentiment analysis on Restaurant Reviews using Natural Language processing and Supervised Learning

It involves in identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral.
Industry: Food and Beverages, E-Commerce
Tools: NLP, Sklearn, Support Vector Machine

Image Classification using Deep Learning

Classifying Images based on the features is a tough problem. With Deep Learning algorithms like CNN it has become fairly easy. In this project we will learn to classify two faces using Convolutional Neural Networks.
Industry: Media
Tools: Deep Learning, Neural Networks, Tensorflow, Keras, CNN

Content Based Recommender Engine using Deep Learning

Content based recommender systems use the content in the data to segment items and then use them for recommending similar items. In this project we will use Deep Learning and Convolutional Neural Networks to perform content-based recommender engine.
Industry: E-Commerce, Retail
Tools: Tensorflow, Keras, CNN, Transfer learning, Deep Learning

Chatbots using Recurrent Neural Networks and Deep Learning

A chatbot is an artificial intelligence (AI) software that can simulate a conversation (or a chat) with a user in natural language through messaging applications, websites, mobile apps or through the telephone. In this project we will build a conversational chatbot using Deep Learning.
Industry: Retail, Banking, E-Commerce, Media
Tools: Tensorflow, Keras, RNN, LSTM, Numpy

Stock market price prediction

This project deals with the predictions of stock market prices using history of Data. It also considers the physical factors vs. psychological, rational and irrational behavior etc. Machine learning techniques implemented in Python acts as game changer for the predictions. Algorithms including Linear regression, LSTM and ARIMA model are used for the same.
Industry: Finance
Tools: Tensorflow, Keras, LSTM, RNN

Exploratory Data analysis of Crime records in Boston

This project analyses data using quantitative prediction of crimes in Boston and drawing visualizations of Trends in the data over the years. Exploratory Data Analysis is carried on the crimes data using lots of techniques from Linear model to Stochastic gradient boosting.
Industry: Defense and Law Enforcement
Tools: Linear Regression, XGBoost, Numpy, Sklearn, Pandas, Matplotlib