Analysis cum Visualisation of Enron dataset and Anomaly detection using k-means clustering
Analysis cum Visualization of Enron dataset and Anomaly detection using k-means clustering
Python Demo
SMS Spam Detection using Neural Networks
SMS spam detection using Recurrent Neural Networks
Implementation details:
1. SMS dataset is taken, which is represented with ham and Spam
2. Dataset is pre-processed
a.removed all stop word, stop words are given in separate file
b. converted type of sms to numeric ham-1 and spam-2
3. Pre-processed dataset is tokenized, applied pad_sequnce, then converted to vectors
4. Then they are trained by network model and creates output file h5, json
5. Test input is given to the model and predicts the output
6. Comparison and accuracy are arrived.
Python Demo
Emoji Emoticons Sentiment Analysis
Emoji Emoticons Sentiment Analysis
Python Demo
Cricket Team Selection by Data Envelopment Analysis
Cricket Team Selection by Data Envelopment Analysis
1. We have listed 15 players in playerid.csv file
2. We are collecting one year average value of batsman, bowler, all rounder
3. We are pre-processing data and storeing the corresponding csv files
4. DEA algorithm is used
input: name, X values (features), Y value (Average)
output: theta for player
5. According to the output theta value, we are getting the 11 players selected
Python Demo
Efficient Metric Indexing for Similarity Search and Similarity Joins
Efficient Metric Indexing for Similarity Search and Similarity Joins
Implementation Details
1. We are using yelp dataset (as it has many features)
2. We are giving query (Restaurants, Phoenix,AZ)
3. Dataset are collected live from yelp data
4. Pre-processing the data to numeral
5. Plot in map
6. We have considered orgin (latitude, longitude), from this 2.5 km surrounded resuts are arrived for space filling
7. The results are plotted in map
8. We apply B+ tree on the space filled results (for the given features, similarity results are arrived)
9. Apply similary join
10. We are processed range query, thus the best matched record is shown as result.
2. We are giving query (Restaurants, Phoenix,AZ)
3. Dataset are collected live from yelp data
4. Pre-processing the data to numeral
5. Plot in map
6. We have considered orgin (latitude, longitude), from this 2.5 km surrounded resuts are arrived for space filling
7. The results are plotted in map
8. We apply B+ tree on the space filled results (for the given features, similarity results are arrived)
9. Apply similary join
10. We are processed range query, thus the best matched record is shown as result.
Python Demo
E-commerce customer prediction
E-commerce customer prediction
Python demo
Credit Card Fraud Detection using Machine learning
Credit card Fraud detection using Machine learning
Python demo
Security Enhancement in ATMs through Helmet Wear Detection
Security Enhancement in ATMs through Helmet Wear Detection
Implementation Details:
1. We are using Object detection method here.
2. We used yolo data set for detecting helmet object
3. The aim of project is giving security alert to bank manager if a person wears a helmet inside ATM machine room
4. The person may carry a helmet also.
5. But we defined our problem of identifying helmet if wear by a person
6. For this we considered finding skin inside the helmet
7. We define a threshold value for skin (if he hold in hands we will not considered as the score value for skin is less) and we detect it
8 SMS alert sent to manager in identification time
Python demo
Test Paper Score Cumulating System Based on Digital Image Processing
Test Paper Score Accumulating System Based on Digital Image Processing
Python demo