ShubhamGaikwad1522/Real-Estate-Prediction

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Created over 4 years ago
Updated over 4 years ago

ShubhamGaikwad1522/Real-Estate-Prediction

ShubhamGaikwad1522

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Real Estate Prediction

Bike-Rental-Analysis

Choosing bike sharing system as a medium of transport will allow an eco-friendlier way of transportation. A bike rental is a bicycle business that rents bikes for short periods of time. Bike rental shops rent by the day or week as well as by the hour, and these provide an excellent opportunity for people like travelers and tourists, who don't have access to a vehicle. Specialized bike rental shops thus typically operate at beaches, parks, or other locations that tourists frequently visit. In this case, the fees are set to encourage renting the bikes for a few hours at a time, rarely more than a day. The objective of this Case is to predict the bike rental count based on the environmental and seasonal settings, so that required bikes would be arranged and managed by the shops according to environmental and seasonal conditions.

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Time-series-analysis-on-international-airline-passangers-using-ARIMA

Time series analysis on international airline passangers using ARIMA

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Prediction-of-liability-of-Personal-loan-prediction

Data Description: The file Bank.xls contains data on 5000 customers. The data include customer demographic information (age, income, etc.), the customer's relationship with the bank (mortgage, securities account, etc.), and the customer response to the last personal loan campaign (Personal Loan). Among these 5000 customers, only 480 (= 9.6%) accepted the personal loan that was offered to them in the earlier campaign.

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Santander-Customer-Transaction-Prediction

At Santander, mission is to help people and businesses prosper. We are always looking for ways to help our customers understand their financial health and identify which products and services might help them achieve their monetary goals. Our data science team is continually challenging our machine learning algorithms, working with the global data science community to make sure we can more accurately identify new ways to solve our most common challenge, binary classification problems such as: • Is a customer satisfied? • Will a customer buy this product? • Can a customer pay this loan? According to past data and from the given problem the output is Classification and it comes under Supervised Machine Learning. We train the model with past data and when the new data is given we predict the outcome.

Bank-Loan-Defaulter-Prediction

Predictive analytics is the stream of advanced analytics which utilizes diverse techniques like mining, predictive modelling, statistics, machine learning and artificial intelligence to analyze current data and predict future. Loans default will cause huge loss for the bank so they pay much attention on this issue to apply various method to detect and predict default behaviors of their customers. The loan default dataset has 8 variables and 850 records, each record being loan default status for each customer. Each Applicant was rated as “Defaulted” or “Not-Defaulted”. New applicants for loan application can also be evaluated on these 8 predictor variables and classified as a default or non-default based on predictor variables.

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