Analysis Of Google Apps Using Python

Last Updated on May 3, 2021

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Course Project of Jovian.ml-

Brief: I took the dataset from Kaggle and did exploratory data analysis using Matplotlib and Seaborn and find answers of many questions like which category of app has highest price, which app have highest rating etc.

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Social Distance Monitoring System(Python And Opencv)

Last Updated on May 3, 2021

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Social distancing is one of the community mitigation measures that may be recommended during Covid-19 pandemics. Social distancing can reduce virus transmission by increasing physical distance or reducing frequency of congregation in socially dense community settings, such as ATM,Airport Or market place .

Covid-19 pandemics have demonstrated that we cannot expect to contain geographically the next influenza pandemic in the location it emerges, nor can we expect to prevent international spread of infection for more than a short period. Vaccines are not expected to be available during the early stage of the next pandemic (1), a Therefore, we came up with this system to limit the spread of COVID via ensuring social distancing among people. It will use cctv camera feed to identify social distancing violations

We are first going to apply object detection using a YOLOv3 model trained on a coco dataset that has 80 classes. YOLO uses darknet frameworks to process incoming feed frame by frame. It returns the detections with their IDs, centroids, corner coordinates and the confidences in the form of multidimensional ndarrays. We receive that information and remove the IDs that are not a “person”. We will draw bounding boxes to highlight the detections in frames. Then we use centroids to calculate the euclidean distance between people in pixels. Then we will check if the distance between two centroids is less than the configured value then the system will throw an alert with a beeping sound and will turn the bounding boxes of violators to red.



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Book Recommendation System

Last Updated on May 3, 2021

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Book Recommendation System

Book recommendation is created and deployed in this approach of work, which helps in recommending books. Recommendation achieved by the users feedbacks and rating, this is the online which analyse the ratings, comments and reviews of user, negative positive nature of comments using opinion mining. User searching for the interested book will be displayed in top list and also can read feedback given by people about the book or any searched items. Whenever user search for any book from the large data available, he gets confused from the number of displayed item, which one to choose. In that case recommendation helps and displays on the interested items. This is the trustworthy approach, which is used in this project where selection is based on the dataset.

Clustering

This project used clustering as the central idea. A clustering approach is used. Clustering is based on similarity where similar elements are kept in a single group. Likewise similar element, the irrelevant elements are also reside in a group, which is another group, based on similarity value or maximum size of cluster. The clustering approach which is used in our work is K-mean clustering for grouping of similar users. It is the unsupervised and simplest learning algorithm, which simplifies mining work by grouping similar elements forming cluster. This is done using a parameter called K-centroids. Distance between each element is calculated for checking the similarity and forming a single cluster to reside the similar elements, after comparing with K-centroid parameter.

In this project, 6 clusters were made.

The project is made with 2 separate datsets in .csv format taken from Kaggle.

  1. Books dataset
  2. Ratings

This project is GUI based. The output page has 2 options:

  1. Rate books
  2. Recommend books

The user can chose either according to themselves.

Rate books

In this option, the user can rate books.

Recommend books

In this option the books are recommended to the user, according to their previous readings.

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Design And Analysis Of Automobile Chasis

Last Updated on May 3, 2021

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Completed under the guidance of Dr. Shailesh Ganpule, Department of Mechanical and Industrial Engineering during August 2019 to November 2019. The objective of this design analysis is to find out the best material and most suitable cross-section for a common “Goods Carrier Truck” ladder chassis with the constraints of maximum shear stress, equivalent stress and deflection of the chassis under maximum load condition. In present the Ladder chassis which are used for making buses and trucks are C and I cross section type, but here we also analysed the Box type and Tube Type. In Trucks generally heavy amounts of loads are carried due to which there are always possibilities of being failure/fracture in the chassis/frame. Therefore Chassis with high strength cross section is needed to minimize the failures including factor of safety in design. The different vehicle chassis have been modeled by considering three different cross-sections namely C, I , Rectangular Box (Hollow) and Tubular type cross sections. The problem to be dealt with for this dissertation work is to Design and Analyze using suitable CAD software and Ansys 19.2  for ladder chassis. The report is the work performed towards the optimization of the Truck chassis with constraints of stiffness and strength. The modeling is done using Solid works, and analysis is done using Ansys 19.2 .. The overhangs of the chassis are calculated for the stresses and deflections analytically are compared with the results obtained with the analysis software. Involved in designing of Heavy Loaded Vehicle chassis in SolidWorks with stress simulation and strain analysis in Ansys. Carried out Failure Analysis using Von Mises Criterion to obtain their sustainability. Performed Convergence Analysis to select the most optimized model with the desired factor of safety. Compared software(practical) value obtained with theoretical value obtained.

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Breast Cancer Analysis And Prediction Using Ml

Last Updated on May 3, 2021

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Project EDA-

Done by using module called Pandas Profiling


Data Set Information:

Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. n the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].

This database is also available through the UW CS ftp server: ftp ftp.cs.wisc.edu cd math-prog/cpo-dataset/machine-learn/WDBC/

Also can be found on UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29


Attribute Information:

  1. ID number
  2. Diagnosis (M = malignant, B = benign) 3-32)

Ten real-valued features are computed for each cell nucleus:

a) radius (mean of distances from center to points on the perimeter) b) texture (standard deviation of gray-scale values) c) perimeter d) area e) smoothness (local variation in radius lengths) f) compactness (perimeter^2 / area - 1.0) g) concavity (severity of concave portions of the contour) h) concave points (number of concave portions of the contour) i) symmetry j) fractal dimension ("coastline approximation" - 1)

The mean, standard error and "worst" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features. For instance, field 3 is Mean Radius, field 13 is Radius SE, field 23 is Worst Radius.

All feature values are recoded with four significant digits.

Missing attribute values: none

Class distribution: 357 benign, 212 malignant

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Logisticregression

Last Updated on May 3, 2021

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Problem Statement :

  • X Education sells online courses to industry professionals. The company markets its courses on several websites and search engines like Google.
  • Once these people land on the website, they might browse the courses or fill up a form for the course or watch some videos. When these people fill up a form providing their email address or phone number, they are classified to be a lead. Moreover, the company also gets leads through past referrals.
  • Once these leads are acquired, employees from the sales team start making calls, writing emails, etc. Through this process, some of the leads get converted while most do not. The typical lead conversion rate at X education is around 30%.

Business Goal:

  • X Education needs help in selecting the most promising leads, i.e. the leads that are most likely to convert into paying customers.
  • The company needs a model wherein you a lead score is assigned to each of the leads such that the customers with higher lead score have a higher conversion chance and the customers with lower lead score have a lower conversion chance.
  • The CEO, in particular, has given a ballpark of the target lead conversion rate to be around 80%.

Strategy

  • Source the data for analysis
  • Clean and prepare the data
  • Exploratory Data Analysis.
  • Feature Scaling ? Splitting the data into Test and Train dataset.
  • Building a logistic Regression model and calculate Lead Score.
  • Evaluating the model by using different metrics - Specificity and Sensitivity or Precision and Recall.
  • Applying the best model in Test data based on the Sensitivity and Specificity Metrics.
  • Solution: 
  • Designed logistic Regression model and calculate the Lead Score 


Key Achievement: 

  • Predicted the leads with a accuracy of 80% and found Important features responsible for good conversion rate or the ones' which contributes more towards the probability of a lead getting converted.
  • Prepared a power point presentation with great visualization for clients and Managers.
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