luggage detection and approval i
Python Projects , Deep Learning Projects

Luggage detection and approval in Airports using CBIR -Content based Image retrieval

Luggage detection and approval in Airports using CBIR (Content based Image retrieval)

1. We have collected luggage dataset from google
2. We are Extracting the features and stored in file called “dataset.csv”
3. Input image is taken
4. Find the object using Coco model (object detection), we identified Luggage objects
5. Cropped the image and stored separately
6. Object is compared with the data set features and using CBIR, we arrive score level
7. If the objects score are <=10, we approved the luggage
8. Similarly done for video
9. Video is taken frame by frame and for every frame it is identified.

 

Python Demo

 

Luggage detection and approval in Airports using CBIR -Content based Image retrieval
stock prediction using clusterin
Python Projects , Machine Learning Projects

Stock Prediction using clustering and classification model

Stock Prediction
1. Data Collection Phase:

a. We have collected real time data from nse website.

b. Any stock with NSE symbol can be given in the textbox “Enter Symbol” Ex. SBIN, HDFC, TCS, INFY

c. Press Submit button to extract real time data from 2000 to present date

d. Feature collected are Date, Symbol, Series, Prev close, Open, High, Low, Last, Close, VWAP, Volume, Turnover

e. Also shown price trend in graph

2. Prediction Phase
——————–

a. We have applied Kmeans Clustering, we have created 3 clusters namely, Cluster0, Cluster1, Cluster2

b. Centroid plot is done for Three cluster

c. Random Forest and Decision Tree algorithm is applied in Clusters for prediction.

d. Predicted value and Real (original) values are ploted.

e. Accuracy and Error values are arrived

3. Forecast Phase
——————
a. Five days forecast using Decision Tree and Random forest is done.

Python Demo

 

Stock Price Prediction using Clustering and Classification model