OASIS INFOBYTE offers a variety of Data Science,Machine learning and AI services. Got the chance for 1 month intern of Data Science. There are 5 task given to complete. After completing 3 tasks, if everything looks fine, the intern will be certified. Glad to be a part of OASIS INFOBYTE intern program.
• Task2_Unemployment_Prediction/: Contains code and resources for the Unemployment Prediction project.
• Task3_Car_Price_Prediction/: Contains code and resources for the Car Price Prediction project.
• Task4_Email_Spam_Detection/: Contains code and resources for the Email Spam Detection project.
Unemployment Analysis with machine learning .Unemployment is measured by the unemployment rate which is the number of people who are unemployed as a percentage of the total labour force. We have seen a sharp increase in the unemployment rate during Covid-19.
• The project code completely done using Python
• Required packages installed, that are pandas, numpy, plotly, seaborn, matplotlib, calendar, datetime.
• This project dataset contains Unemployment in India dataset Unemployment in India.csv and Unemployment_Rate_upto_11_2020.csv link :
https://www.kaggle.com/datasets/gokulrajkmv/unemployment-in-india
To be able to predict used cars market value can help both buyers and sellers. There are lots of individuals who are interested in the used car market at some points in their life because they wanted to sell their car or buy a used car. In this process, it’s a big corner to pay too much or sell less then it’s market value.
• The project code completely done using Python
• Required packages installed, that are pandas, sklearn, keras_tuner, seaborn, tensorflow, tabulate, matplotlib, LinearRegression, RandomForestClassifier, metrics.
• This project dataset contains CarPrice dataset CarPrice.csv link :
https://github.com/amankharwal/Website-data/blob/master/CarPrice.csv
One of the primary methods for spam mail detection is email filtering. It involves categorize incoming emails into spam and non-spam. Machine learning algorithms can be trained to filter out spam mails based on their content and metadata.
• The project code completely done using Python
• Required packages installed, that are pandas, re, nltk, sklearn, seaborn, matplotlib, missingno, wordcloud, collections,Logistic Regression, Decision Tree,RandomForestClassifier, K-Neighbors, Naïve Bayes, SVC.
• This project dataset contains spam dataset spam.csv link :
https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset
This project is licensed under the MIT License - see the LICENSE file for details.
Thanks to OASIS INFOBYTE for the opportunity to participate in this internship. Special thanks to the dataset providers for their valuable contributions.