Machine learning is expected to take over large-scale production in almost every field as it’s constantly evolving. There are hundreds of machine learning projects suggestions which when implemented can save a ton of time via automation. A practical machine learning project can open the doors to newer horizons and improve productivity. In the recent past, massive breakthroughs have resulted in the realm of technology and with these machine learning project ideas, it’s going to make businesses smoother and operations optimal.
Online Fake Logo Detection
This idea is great as it helps in
1. Assisting customers with product verification before making a purchase, thus preventing them from being swindled.
2. The design is user-friendly allows normal people to utilize it.
3. Piracy and Logo copycats can cause confusion among customers, this system will give the firms control over forgeries. However, incorrect input can yield wrong output.
Copied content is a common problem, which makes this project worth it. A detector can be built this way.
1. Loading plagiarized data corpus. Exploring data distribution and existing features. Further, preprocessing and cleaning the data.
2. Defining and extracting features for similarity comparison of answer and source text. Analyzing correlation, selecting features and creating .csv files.
3. Uploading test feature to S3, defining training script plus binary classification model. Train & deploy model via SageMaker then evaluate.
Uber Pickup Analysis
This project can help in identifying patterns as to which hour is the busiest, or the maximum trips/pickups. This can be done as follows
1. Import the dataset and libraries. Categorize into hours and days. The number of pickups should likely increase on weekdays.
2. The hourly data should show fewer pickups from midnight to 6 am, from then increases, making 6 in the evening a peak hour.
3. The data would show Saturday with the least pickups, Sunday, substantially more for leisure, and Monday for the most work-related pickups.
Stock Price Prediction
This project will allow determining the near future value that is held by the stock. Estimation can be done using ml algorithms and long short-term memory (LSTM)
1. Import libraries and start by visualizing the stock data, Print DataFrame shape to detect null values.
2. Select features & set target variables. Make test & training sets, process data for LSTM then build & train the model for predictions.
3. Make comparisons between the true adjusted values and the predicted.
This idea is useful for businesses as many users express their views regarding a product, service or a company/organization. Analyzing a sentiment reveals if a user is satisfied with the product or not, thus rendering what’s hot and what’s not in terms of demand. It can be done as follows
1. Choose a classifier model, import data.
2. Train the analysis classifier by tagging tweets if needed, then test the classifier.
It is an evergreen project that not only maximizes clarity for businesses but also benefits customers. Customer segmentation has numerous types ranging from demographics to psychology, techno-graphics, behavioral, geographical etc. With these steps, customer segmentation can be done.
1. Design, categorize a business case. Prepare data after collecting. Then segment via k-means clustering.
2. Tune the model’s hyperparameters and visualize the results.
The recommendation system is time-saving and efficient for the customers. Correlated items and other varieties are easily accessible. A system of movie recommendations can be built in this manner.
1. Collect data needed for building the model. Reverse map titles and indices.
2. Test the content-based recommending system.
The churn rate is the pace at which entities are opting out of an organization over a period of time. The churn prediction allows identifying the issues of customers, their pain points, and those who are at the highest risk. This prediction requires a workflow, it can be done as follows
1. Define the issues and the objective, gather sources for data such as CRM systems or customer feedback.
2. Prepare data and explore. Further, preprocess it for modeling and tests. Deploy the model and monitor if required.
YouTube Video Classification
Plenty of videos exist on YouTube and without proper classification, they would not be found in searches. The categorization helps to index the videos into relevance. The classification system can be built by
1. Collecting data and setting up, then defining the hyperparameters and preparing the data.
2. Using a network pre-trained for extracting relevant features. Feed data into the sequence model.
This is also an evergreen project. Trying to access the gist from a large piece of article can be time-consuming which brings the need for a text summarizer for quick results. The summarizer can be created in these ways
1. Start with data preparation and then process it, basic clean it. Then do article tokenization into sentences.
2. Further, locate their respective weighted frequency, then do threshold calculation, and generate a summary.
Image Regeneration for old and damaged reels
Doing damaged image repair manually can be a cumbersome task, one that requires skill. But with deep learning, these defects in imagery and reels can be easily corrected via inpainting algorithms. It can help in
1. Colorizing the black and white pics, the areas where pigment has eroded. Different anomalies include tears, holes, scuffs.
2. Pixel values can be altered and old photos can be transformed into a newer edit.
Techniques used for restoration
1. SC-FEGAN: it’s useful in face restoration, filling the void with the most probable pixels.
2. EdgeConnect: it utilizes adversarial edge learning to continue over minor imperfections.
3. Pluralistic image completion: yields various outcomes when dealing with hugs gaps.
Music is a creative human pursuit, however, it can also be generated using LSTM neural networks. This can be done as follows
1. Collect data (royalty-free midi music), use python toolkit music21, Keras for extracting midi files data.
2. Implement LSTM, set the sequence length to 100. Set further 500 note sequences for extending music duration. The repeat feature of this recurrent neural network will generate music.
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