Loan DefaultersLast Updated on May 3, 2021
In this I predict that whether a person ll pay up their loans on time or defaulted with given dataset by Aam Aadmi Party internship project.
Here I had used logistic regression,random forest and Xgboost machine learning model to get better accuracy.
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E-CommerceLast Updated on May 3, 2021
- Implement E-Commerce Web App which had started from 13 November to 12 December 2020.
- In this Web App user can able to purchase the various products which is available in Database and virtually placing the orders.
- This Web App consist of proper Database functionality which help to implement different function and operations.
- User can able to ask any query regarding products and processes , also there is special search functionality in which user can able to filter their required products by simply search on there.
- There are pop-down Cart which shows the product available in the Cart which is select by the user with two buttons in the bottom, one is Checkout and another is Clear Cart.
- On clicking the Checkout button it render the user to the place order page in which user should give all their details by filling the blanks input and finally place the Order.
- All the orders detail of user, orders and query will be stored in the Databases with their Username and Date.
- On clicking the Clear Cart button , it clear all the product which is select by the user for purchase.
Voice Of The DayLast Updated on May 3, 2021
The format used to work well on the radio, so we wanted to recreate those memories on Alexa.
What it does
Players can listen to up to three voice clips of well-known people and/or celebrities talking every day, as well as view a blurred image of the celebrity. After each clip, Alexa will ask you who you think is talking, and you must try to answer correctly. This will earn you a score for the monthly and all-time leader boards. The player can ask Alexa for hints, or to skip that voice clip to move onto the next one. Users scores are awarded depending on how many incorrect answers they gave for that voice and whether they used a hint or not. Users can also ask to hear yesterday’s answers, in case they couldn’t get the answers on that day.
How I built it
To create the structure of the skill, we used the Alexa Skills Kit CLI.
We used Amazons S3 storage for all our in-game assets such as the audio clips and images.
We used the Alexa Presentation Language to create the visual interface for the skill.
We used the Amazon GameOn SDK to create monthly and all-time leader boards for all users to enter without any sign up.
Every day, free users will be given the ‘easy’ clip to answer. The set of clips each day will be selected dependant on the day of the year. Users who have purchased premium gain access to the ‘medium’ and ‘hard’ clips every day, as well as being able to ask for hints for the voices, skip a voice if they are stuck, and enter scores onto the leader boards.
Accomplishments that I’m proud of
As well as creating a high-quality voice-only experience, we developed the skill to be very APL focused, which we are very proud of. The visual assets we used for the project were very high quality and we were able to dynamically display the appropriate data for each screen within the skill. The content loaded depends on who is talking, as well as the difficulty of the voice that the user is answering. APL also allowed us to blur and unblur the celebrity images, as opposed to having separate blurred and unblurred images for each person.
We were also very pleased with how we implemented the GameOn SDK SDK into the skill. When the user submits a score, they have a random avatar created for them, and their score will be submitted under this avatar. This avoids any sign up to use the leader boards, allowing all users to use it easily.
GameOn SDK also allows us to create monthly and all-time competitions/leader boards which all users are automatically entered.
What I learned
I have learnt how to develop APL as well as better practices for structuring it more efficiently. For example, there are many APL views in the project, all of which are almost identical, what I have learnt that would be more effective in future projects would be to condense these down into one primary view that I would use for each screen and just use the appropriate data.
I have also been able to hone prompts to the user for upsells and showing the leader boards. Testing has shown that constant prompts on each play for these things can become tedious to the user, and so we have reduced the frequency of these for a much better user experience.
Age And Gender DetectionLast Updated on May 3, 2021
objective :To build a gender and age detector that can approximately guess the gender and age of the person (face) in a picture or through webcam.
Description : In this Python Project, I had used Deep Learning to accurately identify the gender and age of a person from a single image of a face. I used the models trained by Tal hassner and Gil levi. The predicted gender may be one of ‘Male’ and ‘Female’, and the predicted age may be one of the following ranges- (0 – 2), (4 – 6), (8 – 12), (15 – 20), (25 – 32), (38 – 43), (48 – 53), (60 – 100) (8 nodes in the final softmax layer). It is very difficult to accurately guess an exact age from a single image because of factors like makeup, lighting, obstructions, and facial expressions. And so, I made this a classification problem instead of making it one of regression.
For this python project, I had used the Adience dataset; the dataset is available in the public domain. This dataset serves as a benchmark for face photos and is inclusive of various real-world imaging conditions like noise, lighting, pose, and appearance. The images have been collected from Flickr albums and distributed under the Creative Commons (CC) license. It has a total of 26,580 photos of 2,284 subjects in eight age ranges (as mentioned above) and is about 1GB in size. The models I used had been trained on this dataset.
Working : Open your Command Prompt or Terminal and change directory to the folder where all the files are present.
- Detecting Gender and Age of face in Image Use Command :
python detect.py --image image_name
- Detecting Gender and Age of face through webcam Use Command :
Comcast Telecom Consumer ComplaintsLast Updated on May 3, 2021
Comcast is an American global telecommunication company. The firm has been providing terrible customer service. They continue to fall short despite repeated promises to improve. Only last month (October 2016) the authority fined them a $2.3 million, after receiving over 1000 consumer complaints.
The existing database will serve as a repository of public customer complaints filed against Comcast.
It will help to pin down what is wrong with Comcast's customer service.
- Ticket #: Ticket number assigned to each complaint
- Customer Complaint: Description of complaint
- Date: Date of complaint
- Time: Time of complaint
- Received Via: Mode of communication of the complaint
- City: Customer city
- State: Customer state
- Zipcode: Customer zip
- Status: Status of complaint
- Filing on behalf of someone
To perform these tasks, you can use any of the different Python libraries such as NumPy, SciPy, Pandas, scikit-learn, matplotlib, and BeautifulSoup.
- Import data into Python environment.
- Provide the trend chart for the number of complaints at monthly and daily granularity levels.
- Provide a table with the frequency of complaint types.
- Which complaint types are maximum i.e., around internet, network issues, or across any other domains.
- Create a new categorical variable with value as Open and Closed. Open & Pending is to be categorized as Open and Closed & Solved is to be categorized as Closed.
- Provide state wise status of complaints in a stacked bar chart. Use the categorized variable from Q3. Provide insights on:
- Which state has the maximum complaints
- Which state has the highest percentage of unresolved complaints
- Provide the percentage of complaints resolved till date, which were received through the Internet and customer care calls.
The analysis results to be provided with insights wherever applicable.
Cert-It!Last Updated on May 3, 2021
Cert It! is a web based and android based app that aims to provide and generate certificates over a range of many templates that can be chosen by the user. The user can enter his or her details through a .csv or .xlsx file (containing data in a predefined format having multiple users) or they can list out their own requirements to generate a single certificate.
There are numerous companies and organizations out there that are providing certificates to their participants / winners. Sometimes , even educational organizations have to provide a load of generated certificates to their people. This process gets pretty hectic since its a very repetitive task.
Cert It! aims to solve this problem. We are providing an all round elucidation into this issue by providing an idea that automates these tasks & at the same time keep it user friendly. Through this application we want to provide our users with
- Sample templates of our own on which they can choose and select the best possible fit for their organization and participants.
- Allow the user to upload their own template and generate certificates.
- Allow the user to upload a snapshot of the handwritten data in a specified format through which our app will recognize the necessary details and map it out to generate a certificate.