{"id":25675614,"url":"https://github.com/haripasapuleti/oibsip","last_synced_at":"2026-05-16T04:03:54.806Z","repository":{"id":198276204,"uuid":"700483197","full_name":"HariPasapuleti/OIBSIP","owner":"HariPasapuleti","description":"This Oasis Infobyte internship involves completing data science tasks like classification, prediction, and spam detection using Python. 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Got the chance for 1 month intern of Data Science.\nThere 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.\n\n## Task1 - Iris Flower classification\nThe aim of the iris flower classification is to predict flowers based on their specific features by help of machine learning models.\n\n• The project code completely done using Python\n\n•\tThis project dataset contains iris dataset iris.csv link :\n\n      https://www.kaggle.com/datasets/saurabh00007/iriscsv\n   \n•\tRequired packages are for this project is pandas, numpy, matplotlib, seaborn, pickle, sklearn, Logistic Regression, Decision Tree, K-Neighbors, Naïve Bayes, SVC.\n\n•\tModel trained and tested with supportive models like Decision Tree, K-Neighbors, Naïve Bayes, SVC\n\u003c!-- ## Task2 - Unemployment Prediction\nUnemployment 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.\n\n• The project code completely done using Python \n\n• Required packages installed, that are pandas, numpy, plotly, seaborn, matplotlib, calendar, datetime.\n\n•\tThis project dataset contains  Unemployment in India dataset Unemployment in India.csv and Unemployment_Rate_upto_11_2020.csv link :\n\n     https://www.kaggle.com/datasets/gokulrajkmv/unemployment-in-india\n--\u003e\n## Task2 - Car Price Prediction\nTo 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.\n\n• The project code completely done using Python\n\n• Required packages installed, that are pandas, sklearn, keras_tuner, seaborn, tensorflow, tabulate, matplotlib, LinearRegression, RandomForestClassifier, metrics.\n\n•\tThis project dataset contains CarPrice dataset CarPrice.csv link :\n\n      https://github.com/amankharwal/Website-data/blob/master/CarPrice.csv\n## Task3 - Email Spam Detection\nOne 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.\n\n• The project code completely done using Python\n\n• Required packages installed, that are pandas, re, nltk, sklearn, seaborn, matplotlib, missingno, wordcloud, collections,Logistic Regression, Decision Tree,RandomForestClassifier, K-Neighbors, Naïve Bayes, SVC.\n\n•\tThis project dataset contains spam dataset spam.csv link :\n\n      https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset\n## Task4 - Sales Prediction\nPredicting the sales of a store\n\n• The project code completely done using Python\n\n• Required packages installed, that are pandas, numpy, seaborn, matplotlib, sklearn, LinearRegression.\n\n•\tThis project dataset contains Sales dataset Advertising.csv link :\n\n      https://www.kaggle.com/datasets/bumba5341/advertisingcsv\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharipasapuleti%2Foibsip","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fharipasapuleti%2Foibsip","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharipasapuleti%2Foibsip/lists"}