Prediction Using Unsupervised Ml- The Sparks Foundation Task 2Last Updated on May 3, 2021
This is a clustering project with the required EDA
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A Review On Weather Forecasting Techniques Using Machine LearningLast Updated on May 3, 2021
Weather depicts the atmospheric conditions of a particular place at a particular time. The basic weather elements comprise of temperature, wind, pressure, cloudiness and humidity. Every day, the Meteorological Department prepares weather maps for the upcoming day with the help of the data obtained from various weather stations around the world. Weather forecasts help in taking measures in advance in case of the probability of bad weather and in planning your day ahead.
Different instruments are used to measure various weather elements like, a thermometer is used to measure the temperature, whereas, a barometer is used to measure pressure. Similarly, a wind vane is used to find the direction of wind and a rain gauge is used to measure the amount of rainfall. Thus, with the help of the data collected through these instruments we get the weather forecast in the form of weather charts.
In order to decrease so much manual labour, these weather forecasting techniques are now getting replaced with machine learning models that can predict future weather quite accurately with the help of previously collected data. In this report, we are discussing some of the weather forecasting techniques that are most-likely to be used in order to get accurate weather predictions result. Herein we are comparing the results of the various models, just to get the best results.
Keywords: Weather Forecasting, ARIMA, Holt Linear, Holt Winter, Stationarity, Dickey- Fuller
Project - Mercedes-Benz Greener ManufacturingLast Updated on May 3, 2021
Reduce the time a Mercedes-Benz spends on the test bench.
Problem Statement Scenario:
Since the first automobile, the Benz Patent Motor Car in 1886, Mercedes-Benz has stood for important automotive innovations. These include the passenger safety cell with the crumple zone, the airbag, and intelligent assistance systems. Mercedes-Benz applies for nearly 2000 patents per year, making the brand the European leader among premium carmakers. Mercedes-Benz cars are leaders in the premium car industry. With a huge selection of features and options, customers can choose the customized Mercedes-Benz of their dreams.
To ensure the safety and reliability of every unique car configuration before they hit the road, Daimler’s engineers have developed a robust testing system. As one of the world’s biggest manufacturers of premium cars, safety and efficiency are paramount on Daimler’s production lines. However, optimizing the speed of their testing system for many possible feature combinations is complex and time-consuming without a powerful algorithmic approach.
You are required to reduce the time that cars spend on the test bench. Others will work with a dataset representing different permutations of features in a Mercedes-Benz car to predict the time it takes to pass testing. Optimal algorithms will contribute to faster testing, resulting in lower carbon dioxide emissions without reducing Daimler’s standards.
I have done Data exploration, checking for Missing values and Outliers. Treat the outliers. Applied Label Encoding on categorical variables. I have scaled the data. Applied PCA to reduce the dimension of data but no effect of it on the result. In the prediction, I used Random Forest, KNN, and XGBoost modelling. In all of them, XGBoost has given good result.
Real Estate Price PredictionLast Updated on May 3, 2021
People looking to buy a new home tend to be more conservative with their budgets and market strategies. The existing system involves calculation of house prices without the necessary prediction about future market trends and price increase. The goal of this project is to predict the efficient house pricing for real estate customers with respect to their budgets and priorities. By analyzing previous market trends and price ranges, and also upcoming developments future prices will be predicted. The functioning of this project involves a website which accepts customer’s specifications and then combines the application of multiple linear regression algorithm of data mining. This application will help customers to invest in an estate without approaching an agent. It also decreases the risk involved in the transaction.
Housing prices are an important reflection of the economy, and housing price ranges are of great interest for both buyers and sellers. In this project. house prices will be predicted given explanatory variables that cover many aspects of residential houses. Thus, there is a need to predict the efficient house pricing for real estate customers with respect to their budgets and priorities. This project uses random forest algorithm to predict prices by analyzing current house prices, thereby forecasting the future prices according to the user’s requirements. The goal of this project is to create a regression model that are able to accurately estimate the price of the house given the features.
Indian RailwaysLast Updated on May 3, 2021
- Implement and design an Indian Railway Website which I started from 13th March to 20st March 2021.
- In this website user can able to fetch the proper trains schedule in all over the India like their Arrival Time, Departure Time , Number of Stoppage with their Station Name and Code .
- This website takes the Train Number and Date as an input from the user which he/she want to fetch the details of that particular train and after clicking the Get Schedule button , a number of Flex Cards appeared on the screen on the basis of number of stoppage including the Source and Destination station.
- This cards have two faces one is front which contains the Serial no. at the top and Station Name with their Code at below and another is back , which includes the no. of Days , Arrival Time and Departure Time.
- By default it shows the front side of the card but on hover the card it shows the back side details.
- This project also includes some CSS Animation and Live Timer Clock in the middle of the page.
Heart Attack PredictionLast Updated on May 3, 2021
I did this project in the first semester of my MTech studies at Ahmedabad University. This project is all about predicting the heart attack based on different parameters such as cholesterol, bp, exercise, age, sex, chest pain type, slope, etc. The dataset size was 27 kb. It had 13 columns and 303 rows, I got this dataset from Kaggle. First I did data cleaning in which I removed outliers, null values, duplicate values. After that, I did some data visualization to get insight from the data. During the data visualization, some insights I got from the data were people mostly aged above 40 are suffering/ suffered from a heart attack once in their life, heart rate and chest pain are highly correlated with a heart attack, stress and cholesterol are also one of the main factors of a heart attack, we can see that the patient suffering from heart disease have high cholesterol as compared to the patient not suffering from heart disease. In this project, I have used different machine learning algorithms to predict the Heart attack. I used Logistic regression in which I got 85% accuracy, and decision tree I got 72% accuracy. In the end, there is a decision tree that shows the parameters affecting in order of correlation.