Exploratory Data Analysis:- Task 3 For The Sparks Foundation

Last Updated on May 3, 2021


I did this project during my internship at the sparks foundation. This is an exploratory data analysis project and the dataset was provided by the sparks foundation.

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Movie Website

Last Updated on May 3, 2021



->Problem statement:Create a simple login form where a user can sign in and then there should be one page where user can rate as well as put comments on the movies (you can make list of any 10 movies). The average rating and comments should be visible besides the movie name.


Technology Used:

Front end:



Back end:




Database used:




Steps required to run the code:

-Make sure you have nodejs installed in your computer if not I have mentioned the process to download it in windows below

-Install nodemon by entering npm install -g nodemon in terminal

-Then install mongodb server on https://www.mongodb.com/try/download/community (If already downloaded ignore this step)

-Clone the project in your computer by git clone https://github.com/abhishekdhar30/Internship_assg02 command on terminal.

-Open the terminal having path set to the directory in which project is present

-Run npm install (this command will install all packages present in our json file)

-Run nodemon app.js command

-If you are unable to install nodemon for some reason you can also use <strong>node app.js</strong> instead of above step

-Open browser and set url as <strong>localhost:3000</strong> and press enter this will open our project



-> Installation Steps to download nodejs in windows(This step is for those who don't have installed nodejs in their pc)

-Download the Windows installer from the https://nodejs.org/en/ web site. 

-Choose the LTS version that’s shown on the left. 

-Run the installer (the .msi file you downloaded in the previous step.)

-Follow the prompts in the installer (Accept the license agreement, click the NEXT button a bunch of times and accept the default installation settings).

- Restart your computer. You won’t be able to run nodejs until you restart your computer.

- Confirm that Node has been installed successfully on your computer by opening a Hyper terminal and typing in the commands node --version

You should see the version of node you just installed.



I have created two pages one for login and another for movies.

I have set login page as home page i.e It should be the first page of the project

I have created 10 blocks in movies page for movies in which user can enter ratings and comments and they will be reflect beside to movie name where rating and comments are entered

I have used mongoose mongodb database to store data in database 

The actual work should be like this I have stored the data which user entered in database and when the user click on submit button it will show all data to movies page bcoz I have redirected the page back to movies page 

And I have added one extra feature which is when the user login the page all the previous data in database will be deleted

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Interfacing Of Joystick Using Pic Microcontroller On Lcd Display.

Last Updated on May 3, 2021


This basic design consists of a stick that is attached to a plastic base with a flexible rubber sheath. 

The base houses a circuit board that sits directly underneath the stick. The circuit board is made up of several “printed wires,” which connect to several contact terminals. 

Ordinary wires extend from these contact points to the computer.

The printed wires form a simple electrical circuit made up of several smaller circuits.

 The circuits just carry electricity from one contact point to another.

 When the joystick is in the neutral position – when you’re not pushing one way or another – all but one of the individual circuits are broken.

 The conductive material in each wire doesn’t quite connect, so the circuit can’t conduct electricity.

Each broken section is covered with a simple plastic button containing a tiny metal disc. 

When you move the stick in any direction, it pushes down on one of these buttons, pressing the conductive metal disc against the circuit board.

This closes the circuit and it completes the connection between the two wire sections. 

When the circuit is closed, electricity can flow down a wire from the computer (or game console), through the printed wire, and to another wire leading back to the computer .

When the computer picks up a charge on a particular wire, it knows that the joystick is in the right position to complete that particular circuit. 

Pushing the stick forward closes the “forward switch,” pushing it left closes the “left switch,” and so on. 

The firing buttons work exactly the same way – when you press down, it completes a circuit and the computer recognizes a fire command.

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Enterprise Ai

Last Updated on May 3, 2021


Enterprise AI is about enhancing the customer satisfaction index and to ensure customer stickiness to your organization. By infusing emerging technologies like artificial intelligence to engage and retain the set of customers. Using AI algorithm, we should address the use case. The business operation

processes like determining the customer sentiments from various different media like - Social media, Audio Calls, Video Calls, Images, Emails & Chats, interact with customers to provide quick and effortless

solutions, analyze and learn from buying behaviour to generate next best offer, ascertain customer retention and ensure lesser churn, derive AI-based Customer Segmentation, manage customer

touchpoints, evaluate customer feedback and engage with the customers. We provide a membership card to all the customers who purchase stocks in the store. By scanning the QR code the customer can fill the

feedback. Through the user can easily complete the feedback (Bad, Good, Very good) after purchasing. We are providing three categories (Bronze, Gold and Platinum) for our customers to categorize their

purchasing list to calculate the purchasing efficiency based on their quality ,they purchase. The customer who gives feedback as very good, they come under platinum category, best offers are provided to

them (free purchase for Rs.1000). Notifications will be sent to customers through the messages about the new products available along with its price. Best offers are also provided on festival occasions. We classify the feedback using classification algorithms like random forest to get the positive and negative feedbacks.

Negative feedback will be collected and rectified soon. Through this approach, the shopkeeper is able to get clear feedback about his shop easily.

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Web Base Application Heart Failure Prediction System

Last Updated on May 3, 2021


In this situation, approximately 17 million people kill globally per year in the whole world because of cardiovascular disease, and they mainly exhibit myocardial-exhibit myocardial infarction and heart failure. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body.

In this heart prediction problem statement, we are trying to predict whether the patient's heart muscle pumps blood properly or not using Logistic Regression. In this project, a dataset is downloaded from the UCI repository and this dataset is real. this dataset is collected from one of the most famous hospitals is in the United Kingdom (UK) in 2015 and there are 299 patient records and 12 features(attribute) and one label. Based on that 12 features, we will predict whether the patient's heart working properly or not.

In this problem statement, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning & classifiers to both predict the patient’s survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone.

For model building we use various library packages like Pandas, Scikit learns (sklearn), matplotlib, Seaborn, Tensorflow, Keras, etc., then we will use data description, Data description involves carrying out initial analysis on the data to understand more about the data, its source, volume, attributes, and relationships. Once these details are documented, any shortcomings if noted should be informed to relevant personnel. after that, we use the data cleaning method for cleaning the dataset to check if there are any missing values or not and we split the dataset into training & testing purposes with 70%, 30% criteria. Then the next step is Model Building, The process of model building is also known as training the model using data and features from our dataset. A combination of data (features) and Machine Learning algorithms together give us a model that tries to generalize on the training data and give necessary results in the form of insights and/or predictions. Generally, various algorithms are used to try out multiple modeling approaches on the same data to solve the same problem to get the best model that performs and gives outputs that are the closest to the business success criteria. Key things to keep track of here are the models created, model parameters being used, and their results. And the last step is to analyze the result in this step we check our model score or accuracy by using Confusion Matrix and Model Score. For this model, we got 80% accuracy. In the future, we try to improve that accuracy. For model deployment, we use the python flask and based on that we build the web-based application.

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Machine Learning Implementation On Crop Health Monitoring System.

Last Updated on May 3, 2021


The objective of our study is to provide a solution for Smart Agriculture by monitoring the agricultural field which can assist the farmers in increasing productivity to a great extent. Weather forecast data obtained from IMD (Indian Metrological Department) such as temperature and rainfall and soil parameters repository gives insight into which crops are suitable to be cultivated in a particular area. Thus, the proposed system takes the location of the user as an input. From the location, the soil moisture is obtained. The processing part also take into consideration two more datasets i.e. one obtained from weather department, forecasting the weather expected in current year and the other data being static data. This static data is the crop production and data related to demands of various crops obtained from various government websites. The proposed system applies machine learning and prediction algorithm like Decision Tree, Naive Bayes and Random Forest to identify the pattern among data and then process it as per input conditions. This in turn will propose the best feasible crops according to given environmental conditions. Thus, this system will only require the location of the user and it will suggest number of profitable crops providing a choice directly to the farmer about which crop to cultivate. As past year production is also taken into account, the prediction will be more accurate.


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