- Movie recommendation System Using ML
Building a system that recommends movies is a common and easy project to start with. Such a system will provide suggestions of movies to the users by applying relevant filters based on the user preferences and their browsing history. Here, the user preference is observed in accordance with the data being browsed as well as their ratings. This movie recommendation system will be the result of an implementation of a set machine learning algorithm.
Dataset
You need a dataset to work upon for your movie recommendation system. There are many options to opt from, such as MovieLens, TasteDrive, and so on. Prefer going with a dataset that contains a large number of movies and ratings. You will require the .csv files of the dataset to retrieve the data which is movies and ratings in this case. Now, first of all you will need to do some data pre-processing in order to make the data suitable for use. Once the data is ready, you can implement the appropriate Machine Learning algorithms to suggest movies and even make a record of the most watched genre in your system.
Apart from movie recommendation systems, you can consider making any other type of recommendation system as well, may it be a book recommendation system, cafe recommendation system, etc. You can follow the same procedure with respective dataset for different recommendation systems.
2.Image Cartooning System Using ML
Machine Learning is expanding its grip in every realm so why should cartoonization remain untouched? You can use methods like White Box Cartoonization to convert a real life photo into an animated one. The main idea behind this system is to focus on expression extracting elements to make the process entirely controllable and flexible when it comes to implementing Machine Learning. If we talk about the white box method, it decomposes an image into three cartoon representations, namely, Surface Representation, Structure Representation, and Textured Representation. Further, a GAN (Generative Neural Networks) framework is used for the optimization of our desired result. You can also create emojis out of your own photos using this model. This project, in all likelihood, will take you one step closer to deep learning and computer vision.
If you are looking for a less complex and more comprehensible solution, you can cartoonify an image by building a Python model using OpenCV. You will just need to import ML libraries for the implementation of ML algorithms for image processing and transformation. This project will not only help you improve your skills but also give you a self-made app to edit your photos. How interesting that sounds, right? If you are pretty convinced with this project, start working on it right away!
Dataset
Imagenet, Tbi, ToonNet, and many more online sites are available to supply you with a fine dataset for the training and testing purposes of your ML based model. The dataset will contain specified details of a broad range of images.
3.Iris Flower Classification Project
This is another popular ML project. The basic idea of this project is to classify different species of an iris flower depending upon the length of its petals and sepals. This is a very nice project to deal with machine learning for determining the species of a new iris flower. Machine Learning algorithms are implemented on the dataset of iris flower to draw the classification of its species and work accordingly.
Dataset
The iris dataset consists of 3 classes with 50 instances each. These 3 classes refer to the three types of iris that are setosa, versicolor, and verginica. You can get the dataset for the same online in CSV format. You can have it downloaded from UCI ML Repository as well. Once you have the data set prepared, you will have to choose a neural network for the classification. In the next step you will have to implement the training strategy using ML algorithms. After training your data, you choose the best model with optimum generalisation ability. After getting the most suitable model, you move towards the stages of testing analysis and model deployment. And with this you get your desired system ready.
4.A Dash visualizing and forecasting stock scenario
You must have come across dashboards flashing the stock price charts to help the traders. Stockers actively follow the stock prices of shares of various companies in order to study and analyse the trend, so that they never miss a chance. You can make it easier for the traders by forecasting the price of a stock for a particular date. This project is indeed as interesting to work upon as it sounds. Here, you can use Dash which is a Python framework and some Machine Learning models to create a web application to show the company details and some stock plots. These stock plots will provide the behaviour of a particular stock based on the stock code entered by the user for a given date. The ML algorithms will help in predicting the stock prices.
Dataset
You will need to do stock research to collect data and build your dataset. For that purpose, you can browse through the online trading sites such as Google Finance, StockCharts.com, Merill, etc. Some basic knowledge of Python for machine learning, HTML, and CSS are the prerequisites for this project. Your ML model will do the job of getting the current stock rates and analysing the pricing trends.
5.Data Preprocessing CLI in Machine Learning
As you know, before feeding the dataset to your ML model, you are required to process the data to convert it in algorithm understandable form. Feeding unclean data (data missing attributes, values, containing redundancy, etc.) to your model will lead to drastic results which you would never want. The more vital role data preprocessing plays, the more tedious of a task it is. So, why not build a system on your own to preprocess your dataset for you every time you are up to making a new ML project? This CLI tool will make your other ML projects less time consuming.
This project is nevertheless advantageous in every way. It will not only be helpful for your future projects but also help you mark your expertise in the concepts of OOPs, Pandas, and exception handling. Above all, this project will add much value to your resume.
Dataset
Yelp dataset is a common repository since Yelp made its dataset as open source. You can get all sorts of dataset for your varied assortment of ML projects. You just need to fill an application for and you are free to use their dataset.
6.Super Mart Sales Prediction using Machine Learning
As for a good project alternative, you can create a sales forecasting system for a super mart. The goal will be to build a regression model by implementing ML algorithms to predict the sales of each of the products available in the year ahead. The mart you choose might have established outlets in different regions. Implementation of such a model will help the mart foresee the sales trends and employ suitable business strategies.
Dataset
You can easily get the dataset from the mart you will be making this tool for from its DBA. You will require seeking the sales history of each product in every single store. For example, if we take the BigMart sales dataset, then it comprises 2013 sales in 10 distinct outlets for 1559 products all over. It must also contain certain attributes for every single product and outlet. The dataset that you will use in your project and the information comprised depend on the mart you choose.
7.Loan Eligibility Checker
Another useful and resume boosting project can be a loan eligibility checker system. As we know, before getting a loan, you have to go through a cumbersome process getting your loan sanctioned. Your loan application is approved only if you fit in all the parameters in various circumstances set by the bank. So, this is where a system like Loan eligibility checker can come in handy. If you get to know whether you are eligible for the loan or not beforehand, you can make better preparations to get an approval for your loan.
Dataset
The dataset that you would use for training your ML model will consist of data containing information like sex, marital status, annual income, number of dependents, civil score, qualifications, credit card history and the rest. For this purpose you can get the dataset from the bank you pick for your project. For instance, if you decide to go with Axis Bank, you will use its dataset. You might like to make use of the cross validation method for the testing and training of your data model. This project will help you get a kick start in creating bigger statistical models.
8.Affable Mental Health Tracker
Mental health is a sensitive issue these days. Making a companion app that will keep track of your mental health and ensure your mental wellbeing is definitely a very good option. This project will not only showcase your machine learning skills but represent your holistic and optimistic approach as well. This app will incorporate several personalized tasks and regular progress checks to keep a check on your mental health. You are free to decide what more features you would like to add to this app. Using Flutter is a good option for such an app development. Your Flutter skill coupled with the ML model will help you build a friendly and potential mental health tracker app. Check out the free flutter courses and enroll yourself today.
Dataset
You can get a list of datasets available online for free for mental health phenomenon modelling. It might consist of data from the research papers of various authors. You would probably like to consider going through this link for availing a dataset for this project. You can get your own dataset prepared based on the researches of different bunch of authors on mental health.
9.News Authentication Analysis Model
To put it in simple words, we are talking about making a fake news classification model here. In this huge world of data and social media, the data is transferred at the speed of current. Nevertheless, it takes no time for fake news to spread among the mass. Amidst the bulk of news all around, you can never be sure of the news and judge whether it is fake or authentic at first. This is why this news authentication analysis model can turn out pretty useful. Any fake news will either be linguistic-based or graphic-based. Since it is not always possible to confirm the news authentication by an expert due to sheer volume and speed of data across the internet, you can make your own ML based technique for this task.
This model will apply methods and algorithms based on NLP to identify the fake news in real-time and prevent the havoc that can be caused from the widespread misinformation. All the social media and news platforms will be covered in order to keep an eye on spread of any type of fake news.
Dataset
You can go through the research papers of industry experts available on the internet for the sake of your dataset. The other option is to search for databases like Kaggle database, encompassing news sources and their authentication rates for feeding to your ML model.
10.Wine Quality Prediction Model
Under this project, you will basically be predicting the quality of a wine in accordance with the wine quality dataset. You must have heard people saying, the older the age of the wine, the better it tastes. But, the fact is there are a number of other factors that determine the quality of a wine. These factors include physicochemical tests such as pH value, alcohol quantity, fixed acidity and volatile acidity to name a few. The ML model that you are going to build in this project will analyse the wine quality by exploring its chemical properties.
Dataset
The dataset that you need for this project will incorporate data regarding the chemical properties of different kinds of wine. It will consist of value for various physicochemical tests that will be fed to your ML based model. You can use the publicly available wine quality dataset provided by UCL Machine Learning repository. You can check out the wine quality check research papers available online for collecting the dataset for training and testing of your model.
