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apache-spark-in-python

loan-approval-prediction

Goal of this project is to identify the customers segments those are eligible for loan amount so that bank can specifically target these customers.

machine-learning

market-basket-analysis

Market-Basket-Analysis Objective: Using Apriori Algorithm to do Market Basket Analysis of Customers purchasing behaviors. It can predict what the customer is going to buy next by looking at the products he is buying. Market Basket Analysis is a modelling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items. For example, if you are in an English pub and you buy a pint of beer and don't buy a bar meal, you are more likely to buy crisps (US. chips) at the same time than somebody who didn't buy beer. The set of items a customer buys is referred to as an itemset, and market basket analysis seeks to find relationships between purchases. Frequent Item sets via Apriori Algorithm Apriori function to extract frequent item sets for association rule mining We have a dataset of a mall with 7500 transactions of different customers buying different items from the store. We have to find correlations between the different items in the store. so that we can know if a customer is buying apple, banana and mango. what is the next item, the customer would be interested in buying from the store? Overview Apriori is a popular algorithm for extracting frequent item sets with applications in association rule learning. The Apriori algorithm has been designed to operate on databases containing transactions, such as purchases by customers of a store. An itemset is considered as "frequent" if it meets a user-specified support threshold. For instance, if the support threshold is set to 0.5 (50%), a frequent itemset is defined as a set of items that occur together in at least 50% of all transactions in the database.

matplotlib_visualizations

multiple-linear-regression

python-numpy

python-pandas

python-programs

simple-linear-regression