Black Friday Sales Prediction

• Predicting the amount spend by the customers during the sale of Black Friday following all the steps of a data science lifecycle.
• Also built a web app using the Flask micro-framework and deployed using Heroku.
• Predicting Customer behavior, can impact the decisions taken for the growth of any organization. Using such models, can increase the customer retention and customer satisfaction.

E-commerce-shipping-prediction

• Built an End-to-End Web App using Data Science & Machine Learning for an International E-commerce Company to predict whether their products will reach on committed Delivery Time or not.
• Collected the dataset from Kaggle.
• Preprocessed the data well and built an ML model by tuning the Hyperparameters of RandomForest Classifier using RandomizedSearchCV.
• Saved the Best Performing model in a Pickle file.
• Built a Web App using Python at the Backend with Flask API and HTML & Bootstrap serving at the Frontend.
• Deployed the Web App on Heroku Cloud.
• The Web App receives the data from the User Input, makes predictions with the saved ML model and sends a Prediction text indicating whether the order will reach on time or not.
• Using such models, can increase the customer retention and customer satisfaction.

Zomato ratings prediction!

• Perform extensive Exploratory Data Analysis(EDA) on the Zomato Dataset.
• Build an appropriate Machine Learning Model that will help various Zomato Restaurants to predict their respective Ratings based on certain features.
• DEPLOY the Machine learning model via Flask that can be used to make live predictions of restaurants ratings.

Flight Fare Prediction

• In this project, we'll be analyzing the flight fare prediction using Machine Learning dataset using essential exploratory data analysis techniques then will draw some predictions about the price of the flight based on some features such as what type of airline it is, what is the arrival time, what is the departure time, what is the duration of the flight, source, destination and more.

Fake news detection

• By practicing this python project of detecting fake news, you will easily make a difference between real and fake news.
• This dataset has a shape of 20800x5.
• We ended up obtaining an accuracy of 97.48% in magnitude.

Credit card fraud detection

• The problem statement chosen for this project was to predict fraudulent credit card transactions with the help of machine learning models.
• The dataset is taken from the Kaggle Website website and it has a total of 2,84,807 transactions, out of which 492 are fraudulent.
• The challenge in this project was that, it is highly imbalanced with only 0.17% transactions to be fraudulent.
• The trained model got an accuracy of about 95%.
• Aim is to help the credit card companies to make them capable to recognize fraudulent credit card transactions so, that customers are not charged for items that they did not purchase.

Breast cancer classification using Machine Learning

• In this project, we'll see how can we build a Breast Cancer Classification system using Machine Learning in Python.

Diabetes Prediction

• Objective is to predict whether the patient has diabetes or not based on various features like Glucose level, Insulin, Age, BMI and various other features.
• The web app is developed to help people determining whether, they can be diabetic or not in early stages. Early detection of diabetes can prevent the adverse effects of it.
• Deployed the app using streamlit.

Titanic Survival Prediction

• In this project, we're using a Logistic Regression.
• We will analyze the Titanic data set and make two predictions.
• One prediction to see which passengers on board the ship would survive and then another prediction to see if we would’ve survived.

Movies Movies-Recommendation-Engine

• In this machine learning project, we build a recommendation system from the ground up to suggest movies to the user based on his/her preferences.
• This dataset has a shape of 4803x24
• We built a content-based recommendation engine that makes recommendations given the title of the movie as input.

Prediction using Supervised ML

• Prediction Using Supervised ML In this task we have to predict the percentage score of a student based on the number of hours studied.
• The task has 2 variables where the feature is no. of hours studied and target value is the percentage score.
• This can be solved using Simple Linear Regression.

Wine quality prediction

• In this project, we're predicting the wine quality using Machine Learning that can predict the quality of the wine using some chemical parameters.
• We use the wine quality dataset from Kaggle.
• This dataset has the fundamental features which are responsible for affecting the quality of the wine.
• By the use of several Machine learning models, we will predict the quality of the wine to check further the quality of the wine i.e. is it good or bad.

What is Data Science?February 2nd 2022

The importance of data science brings together the domain expertise from programming, mathematics, and statistics to create insights and make sense of data.