A Machine Learning Case study.
In this project, we are going to predict whether a person’s income is above 50k or below 50k using various features like age, education, and occupation. The dataset we are going to use is the Adult census income dataset from Kaggle which contains about 32561 rows and 15 features that can be downloaded here. We will also build a web application using Flask and deploy using Heroku.
Motivation
Building such predictive models can help us better understand the population of a country as well as the various factors affecting the economy. …
Predicting Sales using Machine Learning.
In this project, we are going to predict how much the customers will spend during Black Friday, using various features such as age, gender, marital status. The dataset we are going to use is the Black Friday dataset from Kaggle which contains about 550068 rows and 12 features that can be downloaded here. We will follow all the steps of a Data Science lifecycle from data collection to model deployment.
WebApp: https://black-friday-sales-prediction.herokuapp.com/
Motivation
Predicting customer behavior is one of the most popular applications of Machine Learning in various fields like Finance, Sales, Marketing. …
An EDA on Indian startup funding dataset.
In the next 7 minutes, we are going to perform exploratory data analysis on the Indian startup funding dataset. Also, we will infer some insights and try to answer certain questions about the Indian startup ecosystem.
Motivation:
India is one of the fastest-growing economies in the world. In the past decade, we have seen a large number of unicorn startups rise in the Indian startup ecosystem which has a global impact. Startups may be small companies but they can play a significant role in economic growth. They create more jobs which mean more employment, and more employment means an improved economy…
Understanding CNNs using Keras.
Contents:
- Motivation.
- Understanding the Dataset.
- Step 0: Import Libraries and dataset.
- Step 1: Data Preprocessing.
- Step 2: Data Visualization.
- Intuition behind ConvNets.
- Step 3: Training the model.
- Step 4: Model Evaluation.
Motivation:
Self-driving cars are becoming very popular due to the efforts of companies like Tesla in automating their electric vehicles. To become level 5 autonomous these cars would have to correctly identify traffic signs and follow traffic rules. After identifying these traffic signs it should also be able to take correct decisions suitably.
Understanding the Dataset:
The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on…
A Machine Learning approach.
In this project, the objective is to predict whether the person has Diabetes or not based on various features like Glucose level, Insulin, Age, BMI. We will use the Pima Indians dataset from the UCI Machine learning repository. We will develop this project in six steps which follows data gathering to model deployment.
Motivation:
Diabetes is an increasingly growing health issue due to our inactive lifestyle. If it is detected in time then through proper medical treatment, adverse effects can be prevented. To help in early detection, technology can be used very reliably and efficiently. …
Aditya Mankar
I talk to computers for a living.
