Iot Home AutomationLast Updated on May 3, 2021
Home automation is perhaps the most talked of IoT projects. IoT-based home automation project aims to automate the functioning of household appliances and objects over the Internet. All the household objects that are connected over the IoT network can be controlled and operated through your smartphone.
This is not only convenient but also gives more power to the user to control and manage household appliances from any location in the world.
This IoT-based project uses a touch-based home automation system. The components of this project include a WiFi connection, an AVR family microcontroller, inbuilt touch sensing input pins. While the microcontroller is integrated with the WiFi modem to obtain commands from the user via the Internet, an LCD screen displays the system status. When the microcontroller receives a command, it processes the instructions to operate the load accordingly and show the system status on an LCD screen.
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Determination Of A Person’S HealthLast Updated on May 3, 2021
Determination of person’s health
The project was built with the intend of helping the society. It has been calculated that approx. 1.9 billion people die due to health-related problems every year. This rate is very high, and the disease is easily preventable
The project has been made with the help of Data Analysis and Machine Learning using Python with a GUI output page. In this project, the machine will analyse the already present data first and then conclude upon a person’s health on his/her given factors.
In this project, gender and either height or weight will be given to the machine. If the height is given then the weight will be predicted and vice-versa. Through these predictions the machine will tell us about the health of a person.
The main goal is to help the society for its betterment as far as health is concerned.
The data set used is from UCI repository. It includes four attributes-
The machine will be trained in these aspects to determine a person’s health or weight and the category it will lie in.
The categories are-
1. 0 – Underweight
2. 1 – Normal weight
3. 2 – Healthy
4. 3 – Over weight
5. 4 – Obesity
The methods followed in chronological form are-
1. Loading dataset (using pandas library)
2. Dataset cleaning (using pandas and numpy libraries)
3. Dataset pre-processing
4. Data visualization (using seaborn, matplotlib and matplotlib.pyplot libraries)
4.1 Univariate analysis
4.2 Bivariate analysis
5. Correlation matrix
The machine learning algorithms applied were-
1. Linear Regression
2. Logistic Regression
3. KNN Classifier
4. Decision Tree Classifier
5. Random Forest Classifier
Random Forest Classifier gave highest accuracy of about 95% while logistic regression gave the leas with about 76%.
The user in the GUI page will be asked:
1. Full name
3. Whether they know their height or weight
4. Their height or weight
Automated Generation Of Videos From News StoriesLast Updated on May 3, 2021
Recent advancements in internet, media capturing, and mobile technologies have let fast growing News industries to produce and publish News stories rapidly. In recent days News industry is trying lot to make their news stories attractive and more engaging to their readers. Youngsters these days often do not have much time to go through an entire news article to understand the content yet they want to know all the important elements the article. Recent surveys suggest that Millennials and other similar age group of people prefer news stories as videos over news as text. However manual generation of videos for each news article is considered costly and laborious. Hence there is a requirement for news video generation system that can create interesting, engaging, concise and high-quality news videos from text news stories with little or no human intervention.
This research will develop an end-to-end automated solution for generating videos from news articles. The system will have different NLP based components for automated news content analysis. Detection of key phrases from the news article will be done using NLP based or Deep learning solutions. Named entities in a news story such as person, time, place, brand etc can be automatically detected using NER for highlighting them in videos. Detection of emotions in news text or phrases for automated suggestion of background music or emojis for video production. In addition, famous tweets related to the news covered by the article can be detected and included in the final video. Also images and videos related to news content should be automatically discovered by crawling from internet and can be instantly used as background scenery in the video. This effort will also consider the analysis of the aforementioned steps in a faster manner for real-time video production.
Bank_Loan_Default_CaseLast Updated on May 3, 2021
The Objective of this problem is to predict whether a person is ‘Defaulted’ or ‘Not Defaulted’ on the basis of the given 8 predictor variables.
The data consists of 8 Independent Variables and 1 dependent variable. The Independent Variables are I. Age: It is a continuous variable. This feature depicts the age of the person. II. Ed: It is a categorical variable. This feature has the education category of the person converted to numerical form. III. Employ: It is a categorical variable. This feature contains information about the geographic location of the person. This column has also been converted to numeric values. IV. Income: It is a continuous variable. This feature contains the gross income of each person. V. DebtInc: It is a continuous variable. This feature tells us an individual’s debt to his or her gross income. VI. Creddebt: It is a continuous variable. This feature tells us about the debt-to-credit ratio. It is a measurement of how much a person owes their creditors as a percentage of its available credit. VII. Othdebt: It is a continuous variable. It tells about any other debt a person owes. VIII. Default: It is a categorical variable. It tells whether a person is a Default (1) or Not-Default (0).
After performing extensive exploratory data analysis the data is given to multiple models like Logistic Regression, Decision Tree classifier, Random Forest classifier, KNN, Gradient Boosting classifier with and without hyperparameter tuning, the final results are obtained and compared on metrics like precision score, recall score, AUC-ROC score.
Government Fund Tracking System Using BlockchainLast Updated on May 3, 2021
The main idea behind the project is to track the funds hierarchically i.e from central government to the common man including in this chain. We have considered four hierarchical components which are: Central government, state government, Contractor, resource provider/dealer. In the beginning, the budgets which would get finalized in the house will be uploaded according to their respective category. After funds allocation state government will instigate the required projects by documenting them and will send the document to the central government. Now the Central government will verify the project details and if satisfied, they will grant the project funds to the state government else they can reject the project. After receiving funds from the central government, the state government will open the tenders for the contractor and by proper bidding system the contractor will be chosen for the specific project. As bidding and tender allocation will be carried out by an automation bidding system with no human intervention involved, it would reduce corruption. Government committee will check the amount of work done synchronously and will mark every progress by submitting a brief report to the hierarchical officer, who will add it to the blockchain. In this report the progress can be portrayed in the form of images, videos, written plan of the building or structure, etc. To get the payment the contractor will have to submit a form of his total spendings with proper distribution over the duration. This form details will then be checked by the respective authority of the state government and then will initiate the payment to the contractor. In this way doing work over a period gets paid, this process will repeat until a particular work is being done completely.
Churn PredictionLast Updated on May 3, 2021
Predicting Customer Churn at a Fictitious Wireless Telecom Company
Churn Management has gotten great attention among the telecommunication Industry because it is proved that instead of going for advertisements to find new customers it’s better to find a technique, solution, and all the available resources in our service to figure out a pattern to make customers stay in the company. Every telecommunication company has huge competition and due to easy access of the plans and services provided by all the companies, a customer can switch the company anytime. For churn Prediction, it is most required to identify the customer who has the highest probability of leaving the service of the company and it will be effective if it’s done at the right time. Through this company can make a decision on what service to provide to make the customer not leave the service.
Here, we can reformulate the given problem as a Classification problem. My goal is to build a Classification model that can predict if Customers will stay with the company or not from the given features. To achieve this, first, I did data analysis and data cleaning, data preparation for training, and then model building. After this, based on the performance I find the best parameters of our model through GridSearchCV which best suits for the given data and gave the expected result.