Air Quality Index(2020) AnalysisLast Updated on May 3, 2021
I started it with 230+ datasheets from across the country. It is a data preprocessing & data visualisation project. There was a lot of preprocessing required. The sheer number of files was a challenge in itself. Preprocessing was huge & time-consuming. Feature Selection was an important aspect. Data visualisation cannot be performed on all features & not all attributes would give presentable conclusion conclusions. The reason for this is that there are only some attributes for which the AQI index is available(a fact that I learned after preprocessing all the files).
Since there are so many different files, I have only provided modular code or a prototype code per se for data cleaning and visualisation.
An example image is also given in the attachment below. All graphs created were like this only with some variation as per available data.
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In this Project, when you enter data, the browser and/or the web server will check to see that data is in the correct format and within the constraint set by the application.If the information is correctly formatted, the application allows the data to be submitted and saved in a database.
This project is done by using validation attributes on form elements .These are:-
1 . required :- Specifies whether a form field needs to be filled in before the form can be submitted.
2. type :- Specifies whether the data needs to be a number, an emaill address, or some other specific preset type.
3. pattern :- Specifies a regular expression that defines a pattern the entered data needs to follow.
In this Project, after giving your personal information like your name, your address,etc, and then if you click on submit button , then the information which you have filled will be shown .
If you want to update some information ,then you can also do by clicking on update button.The updated information will be shown.
If you want to delete some record ,then you can also delete it by clicking on delete button.
Duration of Project:- 10 months
My role in this project is of Developer
Encrypt And Decrypt Strings And Images Using PythonLast Updated on May 3, 2021
Encrypt and Decrypt Strings
Encryption is the process of encoding the data. i.e converting plain text into
ciphertext. This process is the encryption. And Decryption is a process of decoding the encoded data. Converting the ciphertext into plain text. This process requires a key that we used for
We require a key for encryption. There two main types of keys used for encryption and decryption. They are Symmetric-key and Asymmetric-key.
In symmetric-key encryption, the data is encoded and decoded with the same key. This is the easiest way of encryption, but also less secure. The receiver needs the key for decryption, so a safe way need for transferring keys. Anyone with the key can read the data in the middle.
Asymmetric-key Encryption, we use two keys a public key and private key. The public key is used to encrypt the data and the private key is used to decrypt the data. By the name, the public key can be public (can be sent to anyone who needs to send data). No one has your private key, so no one the middle can read your data.
Encrypt and Decrypt images
simple process in which we convert our data or information into secrete code to prevent it from unauthorized access and keep it private and secure.
First, we will select an image, and then we will convert that image into a byte array due to which the image data will be totally converted into numeric form, and then we can easily apply the XOR operation on it. Now, whenever we will apply the XOR function on each value of the byte array then the data will be changed due to which we will be unable to access it. But we should remember one thing that here our encryption key plays a very important role without that key we can not decrypt our image. It acts as a password to decrypt it
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.
Loan PredictionLast Updated on May 3, 2021
A Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have provided a dataset to identify the customers segments that are eligible for loan amount so that they can specifically target these customers. So in this project our main objective is to predict whether a individual is eligible for loan or not based on given dataset.
For simplicity i divided my projects into small parts-
- Data Collection :- I collected data from 'Anylitical Vidhya' as a CSV file. We have two CSV file one is train data which is used for training the data and other is test data which is used for prediction based on training of model.
- Import Libraries:- I import differnt Sklearn package for algorithm and different tasks.
- Reading data:- i read the data using pandas 'read csv()' function.
- Data Preprocessing -: In this part i first found missing values then i remove a column or imputed some value (mean, mode, median) According to the amount of data missing for a particular column.
I checked the unique value in each column. Then i did label encoding to convert all string types data to integer value. I used dummie function to convert each unique value to different columns . I find out correlation matrix which shows the correlation between columns to each other.
Then i split the data. I did analysis on each column and row of dataset.
Here i selected a classifier algorithm because it is a classification problem i.e. in this problem target value is of categorial datatype.
Then i create a model . I trained that model using Logistic regression Algorithm , which is a classification algorithm. I feed training dataset to model using Logistic regression algorithm. After creating model i did similiar data preprocessing to test dataset . And then i feed test dataset to trained model which predict the values of this test dataset. And then i found accuracy of this model using actual target value which is given in training dataset. and predict target value which we predict from test dataset.
After this i used another algorithm which is random forest classifier. i did traied the model using random forest classifier and then calculate the accuracy.
I compared the accuracy of both algorithm and i preffered algorithm which had better accuracy.
In this project i got 78.03% accuracy when i create model using random forest classifier and got 80.06% when i create model using logistic regression.
Identifying Water Sources For Smallholder Farmers With AgriLast Updated on May 3, 2021
CIAT and The Zamorano Pan-American Agricultural School, in coordination with the United States Agency for International Development (USAID)/Honduras, began in March the validation and dissemination process of the geographic information system (GIS) tool AGRI (Water for Irrigation, by its Spanish acronym).
What is AGRI?
AGRI was developed in ArcGIS 10.1® for western Honduras with the aim of providing support for decision making in identifying suitable water sources for small drip irrigation systems. These systems cover areas of up to 10 hectares and are part of the U.S. government initiative Feed the Future in six departments of western Honduras (Santa Bárbara, Copán, Ocotepeque, Lempira, Intibucá, and La Paz).
AGRI identifies surface-water sources and sites suitable for rainwater harvesting for agriculture. In addition, AGRI maps the best routes for installing water pipes between the first parcel of the irrigation system and the identified water source. The tool is complemented by deforestation analyses of upstream areas, as an indicator of watershed conservation status.
How was AGRI developed?
Developing this tool required the implementation of a complex framework of spatial analysis that included correcting the terrain Digital Elevation Model (DEM), using weather information derived from remote sensors, hydrological analysis such as estimation of runoff and water balance, and modeling the path with lower costs or fewer difficulties in installing pipes across the landscape. Additionally, it was necessary to do digital soil mapping for some variables.
What does AGRI offer to its users?
AGRI was developed based on the following needs identified by USAID-Honduras in relation to the implementation of small irrigation systems in the country:
- To find the closest water source that permits transportation of the water by gravity to parcels.
- To search for “permanent and sufficient” water sources to establish water outlets.
- To find suitable sites for building reservoirs for the harvest of runoff water.
- To take into account the protection of water sources for human consumption and other protected zones and avoid possible conflicts on water use.
- The tool needs to be easy to use for technicians and agronomists.
- The tool should use information that is readily available in the country.
This application was developed at the request of USAID-Honduras and it responds to the implementation needs of its programs. This implementation was led by the Decision and Policy Analysis (DAPA) area of CIAT with the participation of the soil area, which contributed with the digital soil mapping for the project. Likewise, Zamorano University supported the field validation and the analysis of the legal context related to water use, which serves as a basis for the application of this tool.