Taranjeet0874/Market-Basket-Optimization-Apriori-Analysis-ML

Stars
1
Language
Jupyter Notebook
Created almost 4 years ago
Updated almost 4 years ago

Taranjeet0874/Market-Basket-Optimization-Apriori-Analysis-ML

Taranjeet0874

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

The Apriori algorithm uses frequent itemsets to generate association rules, and it is designed to work on the databases that contain transactions. With the help of these association rule, it determines how strongly or how weakly two objects are connected. Frequent itemsets are those items whose support is greater than the threshold value or user-specified minimum support. It means if A & B are the frequent itemsets together, then individually A and B should also be the frequent itemset. Suppose there are the two transactions: A= {1,2,3,4,5}, and B= {2,3,7}, in these two transactions, 2 and 3 are the frequent itemsets. In this project the transactions of a store is noted for a week and apriori algorithm is used to make frequent item data so that a proper decision can be taken regarding those items which helps to make better offers for the customers which helps in increasing profit for the store.

Car-Dekho-Price-Prediction-Regression-ML

In this project prices are predicted for various 2nd hand car avaliable on car dekho website using regression.

Jupyter Notebook

Applied-Social-Network-Analysis-in-Python

This is the fifth course of 5 course specialisation in Applied Data Science in python.

Jupyter Notebook

Flight-Fare-Prediction-regression-models

After cleaning the data ,and encoding the categorical variables ,removing the outliers,and seeing feature importance and then depicting the flight fare of national flights with the help of regression models

Jupyter Notebook

Data-Structures-and-Algorithms

These are the algorithms in C for the subject Design and Analysis of algorithms.

Password-Strength-Prediction-NLP

Its a password strength prediction model designed with the help of TF-IDF vectorizer which predicts whether the password strength is good or not

Jupyter Notebook

Email-Spam-Classifier-Naive-Bayes-NLP-ML

The SMS Spam Collection is a set of SMS tagged messages that have been collected for SMS Spam research. It contains one set of SMS messages in English of 5,574 messages, tagged according being ham (legitimate) or spam. The files contain one message per line. Each line is composed by two columns: v1 contains the label (ham or spam) and v2 contains the raw text. It will predict the correct classification of emails as SPAM or HEM ,developed with analysing on SMS Spam collection dataset

Jupyter Notebook

Applied-Text-Mining-in-Python

This is the fourth course of 5 course specialization in Applied Data Science in python.

Jupyter Notebook

Restaurant-Reviews-Analysis-NLP-ML

In this project sentiment Analysis of Restaurant reviews has been done. Firstly proper sentiment and tokenization of reviews has been done. all stopwords are removed then Naive Bayes Model has been trained with training data ( some reviews ) then we make predictions for the test data whether the reviews are negative or positive so it is a classification problem which is done using gaussian naive bayes.

Jupyter Notebook

Chicago-Crime-Rate-Prediction-FBPROPHET-ML

The Chicago Crime dataset contains a summary of the reported crimes occurred in the City of Chicago from 2001 to 2017. Dataset has been obtained from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. Dataset contains the following columns: ID: Unique identifier for the record. Case Number: The Chicago Police Department RD Number (Records Division Number), which is unique to the incident. Date: Date when the incident occurred. Block: address where the incident occurred IUCR: The Illinois Uniform Crime Reporting code. Primary Type: The primary description of the IUCR code. Description: The secondary description of the IUCR code, a subcategory of the primary description. Location Description: Description of the location where the incident occurred. Arrest: Indicates whether an arrest was made. Domestic: Indicates whether the incident was domestic-related as defined by the Illinois Domestic Violence Act. Beat: Indicates the beat where the incident occurred. A beat is the smallest police geographic area – each beat has a dedicated police beat car. District: Indicates police district where the incident occurred. Ward: The ward (City Council district) where incident occurred. Community Area: Indicates the community area where the incident occurred. Chicago has 77 community areas. FBI Code: Indicates the crime classification as outlined in the FBI's National Incident-Based Reporting System (NIBRS). X Coordinate: The x coordinate of the location where the incident occurred in State of Illinois. Y Coordinate: The y coordinate of the location where the incident occurred in State of Illinois. Year: Year the incident occurred. Updated On: Date and time the record was last updated. Latitude: The latitude of the location where the incident occurred. This location is shifted from the actual location for partial redaction but falls on the same block. Longitude: The longitude of the location where the incident occurred. This location is shifted from the actual location for partial redaction but falls on the same block. Location: The location where the incident occurred. predicting what crimes could take place in future by studying the crime rates in different regions ,with the help of fbprophet time series analysis .The visualization is made with the help of seaborn and matplotlib library ,by plotting several countplots,histograms etc

Jupyter Notebook