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Time-Series-Analysis-and-Forecasting
Build time series modelling and evaluation the best model. Build model like ARIMA, Exponential Smoothing, TBATS, NN and others. Compare the best model forecast the next 30 , 60 data points.Predicting-a-Player-s-position-based-on-the-attributes
Predicting a Player’s position based on the attributes Using the data, create a model that accurately predicts/assigns a players position based on the individual attributes. The following steps should be clearly elucidated: 1. Data Cleaning 2. Features considered for EDA and further steps. 3. Exploratory Data Analysis Undertaken 4. Inference from EDA 5. Choice of Best Algorithm and Why 6. Training Accuracy 7. Predictions with test dataText-topic-modelling
Predict if a particular sentence in an article should be included in the summary of the article or not (Incorporates NLP Along with ML)Twitter-Data-Analysis
Web Scraping tweets from my personal api. Using R Language for web Scraping and analysis and Visualization.Network-fault-incident-prediction
https://docs.google.com/document/d/1gyzClRjjgGy2lyoqyhqAO1amBIEEDmbXRx_-tqmdMDs/viewTime_Series-Analysis-Using-Python
olt(Optical Line Termination) forecasting, build time series modelling for forecasting.Churn-Analysis-and-Prediction-Modelling
Customer Behavior, Feature Selection, churn prediction. Develop ML model for churn Prediction. Used ML model Like LR, LDA, KNN, CART, NB, RF, XGB, AdaBoost, SVM.Sentiment-analysis-for-Amazon-reviews-data
Python-Script-for-dump-excel-file-within-Database.
Dump excel file within Database. Use MongoDb database. Site wise collection dump into the database. Use library pymongo, pandas, datetime, jason, time and others. At first connect with database and load the data. Then dump the data specific site wise collection.Ernst-Young-Case-study-of-ML
Problem: Predict the sales demand for consumer goods. Data: Attached is a spreadsheet containing sales data. The attached document contains instruction and clarification about the data. Please follow the instructions and prepare the output file. Case Study will be evaluated on the below criteria Data Processing Feature Engineering Code AutomationPredicting-Future-Sales
Provided with daily historical sales data. The task is to forecast the total amount of products sold in every shop for the test set. Note that the list of shops and products slightly changes every month. Creating a robust model that can handle such situations is part of the challenge.Recharge-Use-Cases-Analysis
Subscriber wise daily usage analysis and Bucketing daily typical use wise. Clustering several group of Bucket for recommendation Daily typical usage per subscriber over telecom network.Love Open Source and this site? Check out how you can help us