Imdb : Movie Reviews Sentiment Analysis | Natural Language Processing

Last Updated on May 3, 2021


Preprocessed and cleaned the movie reviews by viewers using the concept of Regular Expressions and Text Cleaning on raw data in python.

Pioneered the implementation of TFIDF Vectorizer & applied SVM classifier in the prediction of sentiment.

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Quiz App In Android

Last Updated on May 3, 2021


§ The “QuizApp” has been developed to override the problems prevailing in the practicing manual system


§ This App is supposed to eliminate and in some cases reduce the hardships faced by this existing system. Today internet become reality and usage of internet become very much popular and there is tremendous increase of internet in all over the world for education purpose.


§  The QuizApp provides complete functionality of evaluation and assessing student’s performance skills. Quiz Application can lead to error free, secure, reliable and fast.


§ The Quizzes will form the backbone of the automated process and will play an important role in  generation of unique sets of questions.


The Quiz application  is used for conducting quiz for students or this software can also be used by the company for the recruitment process.

At first, the student is needed to register his/her name along with all the information needed and need to select username and password for the login process. Using this username and password, the student can login into the “QuizApp” App. 

Next procedure is answering the quiz. As soon as the student selects the Category and set, the questions with 4 options will be displayed.

The student has to select any one option and click on next option. This will continue till the end of the question. At the end final result will displayed to user.

Student has facility to bookmark question for further reference.

  • Immediate Results
  • Timer
  • Category wise Question Set
  • Bookmarks
  • Scorecard
  • Authentication using Firebase

More Details: Quiz App in Android

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Regression Model

Last Updated on May 3, 2021



It is a supervised technique, regression models are used to predict a continuous value, the goal of regression model is to build a mathematical equation that defines y as a function of the x variables.

Y = mX + B , where X is independent variable and Y is dependent variable.

}Eg; Salary of employee based on years of experience. In equation (Y = mX + B) , Y is salary, m is slope, X is years of experience, B is y intercept.  


  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Linear Regression
  • Support Vector Regression(SVR)
  • Decision Tree Regression
  • Random Forest Regression 

Table of contents

  • Importing the libraries:
  • Numpy for working with arrays, linear algebra, Matplotlip for plotting using its module pyplot , Pandas for data manipulation and analysis.
  • Importing the dataset:

Create a new variable (dataset) , call certain functions from pandas library . Dataset = Pd.read_csv(‘data.csv’).

  • Taking care of missing data:

 Using Scikit-learn it is a library that provides many unsupervised and supervised learning algorithms. By using imputer we are filling the missing data by average of the column. Fit-method identifies the missing data and calculate mean, Transform-method will replace the missing by average salary.

  • Encoding Categorical Data:

Using one hot encoding it is a representation of categorical variables as binary vectors using integers.

  • Splitting the dataset into the Training set and Test set:

Training set = training ML model on existing observations.

Test set = performance on new observations

Creating 2 entities Y(independent) salary, X (dependent) years of experience.

  • Feature scaling:

it is performed so that some features don’t dominate other features. It is done after splitting to prevent data leakage, from sklearn model selection is used which creates 4 separate sets i.e 2 for training, 2 for test.

  • 2 techniques of feature scaling are : standardization, normalization.
  • Training the model on the Training set
  • Predicting the Test set results
  • Visualizing the Training set results
  • Visualizing the Test set results

Open Source Project Of a Real Life Example

}1) Simple Linear Regression

}Problem description: Dataset with 30 observations, goal is to build a simple linear regression model to understand correlation btw years of experience and salary to predict the salary for new employee with specific years of experience. 

Table of contents:

}Importing the libraries

}Importing the dataset

}Splitting the dataset into the Training set and Test set

}Training the Simple Linear Regression model on the Training set

}Predicting the Test set results

}Visualizing the Training set results: in graph the regression line is as close as the actual salaries.

}Visualizing the Test set results: the predicted salaries are close to actual salaries .

The dataset:


2) Multiple Linear Regression

}Problem description: IN Data set of 50 start-ups, one should invest in which company to achieve the goal of maximizing profit .

}Table of contents:

}Multiple Linear Regression

}Importing the libraries

}Importing the dataset

}Encoding categorical data: using one hot encoding (Yew York = 001, California = 100, Florida = 010)

}Splitting the dataset into the Training set and Test set

}Training the Multiple Linear Regression model on the Training set

}Predicting the Test set results: test set has 20% of whole dataset so 20% of 50 startups = 10 observations ; we get 2 vectors

}1st is of 10 real profit from test set , 2nd 10 predicted profit of same.

}Then concatenate (from numpy concatenate 2 vectors predicted, real )

}In the result on the left we have the first vector = predicted profit (Y_pred), on the right we have secound vector = real profit (Y_test).

}Hence MLR is well adapted to this dataset 

The dataset:


Polynomial Linear Regression

}Problem description: 10 observations in the dataset. The previous salary mentioned by candidate is 16k Goal is to predict the previous salary of the candidate also the present salary to be provided according to the post applied so that we know the salary mentioned by candidate is bluff or true.

}Table of contents:

}Importing the libraries

}Importing the dataset

}Training the Linear Regression model on the whole dataset

}Training the Polynomial Regression model on the whole dataset

}Visualizing the Linear Regression results: on graph red is the real salary and blue is regression line with predictions, linear model is not adapted as far from prediction.

}Visualizing the Polynomial Regression results: better result as the greaater the degree the better is the prediction.

}Visualizing the Polynomial Regression results (for higher resolution and smoother curve): increase the number of points on x axis.

}Predicting a new result with Linear Regression: predicted salary is 330378.78 >16k hence bad prediction.

}Predicting a new result with Polynomial Regression: : predicted salary is 158862.45 approx 16 so acceptable.  

The dataset: