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Getting started with machine learning: Projects

In this blog we will be discussing how to approach machine learning projects at beginner, intermediate and advanced level.

If you haven't read the previous blog in this series, you can read it here.

In that blog we discussed the prerequisites and the flow of process for beginning the journey in the field of machine learning.

We have discussed the steps involved in the machine learning projects, which are listed below:

  1. Importing necessary libraries and packages

  2. Data preparation/ pre-processing

  3. Splitting of data

  4. Training model

  5. Fine/tuning the model

  6. Evaluation of model

  7. Making predictions

Following these steps in a chronological order is a systematic and organized approach towards building machine learning models.

These steps suits all levels of machine learning projects be it beginner, medium or advanced level.

Knowing these steps in theory and dealing with them in practical situations are two very different aspects. For example, in theory we know that data pre-processing means cleaning and transforming the data in a manner that it becomes suitable input for the model. But when we actually start with practicing data pre-processing, we have to learn how to perform data cleaning i.e. dealing with missing values, keeping only the relevant attributes, dealing with skewness in data etc.

Therefore practice becomes extremely necessary to build a better understanding of concepts in machine learning.

Generally, when we start learning something new we start small and learn that small thing really well. We then keep expanding our horizon little bit at a time. This way one can build up his/her knowledge by accumulating these small feats. And this is how we should proceed in this case.

We should start with very basic and easy projects to begin with.

Projects for Beginners

In this section, we will discuss some projects that beginners can work on to get a better grasp of the subject. These projects are most suitable for people who are new to the domain of machine learning.

The solution of the following projects in this section are easily available on Kaggle with explanation to better facilitate the process of learning.


  • Boston Real Estate Price Prediction In this project the objective is to predict the prices of houses in Boston based on a number of features such as no. of rooms, zone, built years etc. The dataset used for this project is called Boston housing dataset and it is available in scikit learn. This dataset has 13 attributes/features and 1 target with a total of 506 samples only. This dataset can be used for practicing simple linear regression as well as multiple regression. If you wish you can try other types of regression for making a comparative analysis.

  • Red Wine Quality Prediction In this project the objective is to predict the quality of wine based on composition of various chemicals present in it. The dataset used for this project includes info about the chemical properties of different types of wine and how they relate to overall quality.


  • Iris Flower Classification: It is called the 'Hello World' program of machine learning and it’s a classification problem where the species of the iris flower is predicted based on dimensions of its petals and sepals. The dataset used for this project consists of 150 instances of petal length, petal width, sepal length, sepal width and the name of the corresponding species. The no. of instances per species is equal.

  • Titanic Survival Prediction In this project the objective is to build a predictive model that can give a solution to the question, “What types of people were more likely to survive?” using passenger data. Each row in the dataset represents one person and the columns describe different attributes/details about a person such as: whether they survived, their age, passenger-class, sex, no. of parents/children/siblings/spouses aboard the ship, the fare they paid, ticket number, cabin number etc.


  • Iris Flower Classification: This project aims at finding identify clusters in the dataset which belongs to one of the three species of Iris flowers based on the dimensions of their petals and sepals. The only difference in this case is that the labels are not present before hand and needs to be determined by examining the characteristics of clusters. The dataset used for this project consists of 150 instances of petal length, petal width, sepal length, sepal width only.

  • Credit Card Customer segmentation: The objective of this project is to identify different segments in the existing customer based on their spending patterns as well as past interaction with the bank. The dataset consists of information on various customers of a bank with their credit limit, the total number of credit cards the customer has, and different channels through which customer has contacted the bank for any queries, different channels include visiting the bank, online and through a call.

Intermediate Level Projects:

  • The Black Friday Project: The objective of this project is to predict the amount likely to be spent by customers in Black Friday Sale depending on features such as gender, age, and occupation. The dataset consists of 550,000 observations about Black Friday sales, which are made in a retail store.

  • Music Genre Classification Machine Learning Project: The objective of this project is to develop a machine learning model to automatically classify different musical genres from audio. The dataset used for this project is called The GTZAN genre collection dataset that was collected in 2000-2001. It consists of 1000 audio files (.wav format) which are 30 seconds long. There are 10 classes ( 10 music genres) each containing 100 audio tracks.

  • MNIST Digit Classification Project: The objective here is to enable machines to recognize handwritten digits. The dataset used for this project is called The MNIST dataset. This is probably one of the most popular datasets among machine learning and deep learning enthusiasts ( 10 classes). It contains 60,000 training images (28x28) of handwritten digits from zero to nine and 10,000 images for testing.

  • Credit Card Fraud Detection Project: The objective in this project is to build a fraud detection model on credit cards. You need to use the transaction and their labels to detect if new transactions made by the customer are fraud or not. The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, the original features and more background information about the data is not provided. The provided features are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'.

  • Fake News Detection Project: The objective of this project is to distinguish fake news from real ones. Fake news spreads like a wildfire and this is a big issue in this era. You can use supervised learning to implement a model like this. The fake news detection dataset ( aka LIAR : a benchmark dataset for fake news detection) contains 13 variables/columns for train, test and validation sets in CSV format.

  • Real-time Spam Detection: The objective of this project is to distinguish between spam (illegitimate) and ham (legitimate) messages in real-time. The SMS Spam Collection is a set of SMS that have been collected for SMS Spam research. It contains one set of SMS in English of 5,574 messages, tagged according being ham (legitimate) or spam. The files contain one message per line. Each line is composed by two columns: v1 contains the label (ham or spam) and v2 contains the raw text.

Advanced Level Projects:

  • Amazon Product Reviews Sentiment Analysis: The objective of this project is to study the correlation between the Amazon product reviews and the rating of the products given by the customers. The dataset contains the product reviews of over 568,000 customers who have purchased products from Amazon.

  • Speech Emotion Recognition Project: This is one of the best machine learning projects. The speech emotion recognition system uses audio data. It takes a part of speech as input and then determines in what emotions the speaker is speaking. You can identify different emotions like happy, sad, surprised, angry, etc. This project could be helpful for identifying customer emotions during the call with the call center. The dataset used is called the RAVDESS dataset, this is the Ryerson Audio-Visual Database of Emotional Speech and Song dataset. This dataset has 7356 files rated by 247 individuals 10 times on emotional validity, intensity, and genuineness. The entire dataset is 24.8GB obtained from 24 actors.

  • Movie Recommendation System using Machine Learning: The main goal of this machine learning project is to build a recommendation engine that recommends movies to users based in their preferences. The dataset used is called the the MovieLens Dataset. This data consists of 105339 ratings applied over 10329 movies. The dataset is in the form of two csv files movies.csv and ratings.csv.

  • Text Summarization Projects: The objective in the project is to produce summary by interpreting the text using advanced natural language techniques in order to generate a new shorter text — parts of which may not appear as part of the original document, that conveys the most critical information from the original text, requiring rephrasing sentences and incorporating information from full text to generate summaries such as a human-written abstract usually does. The one of the datasets that can be used for this project is the wikihow dataset that contains around 200,000 long sequence pairs of articles and their headlines. This dataset is one of the large-scale datasets available for summarization with the length of articles varying considerably. These articles are quite diverse in their writing style which makes the summarization problem more challenging and interesting.

  • Image segmentation Projects: The objective is to train a Neural Network which can return a pixel-wise mask of the image. It is used to make representation of an image into something that is more meaningful and easier to analyze. One of the dataset that can be used for this project is the Oxford-IIIT Pets dataset.

  • Topic modelling Projects: In machine learning and natural language processing, topic modeling is a type of statistical model for discovering abstract subjects that appear in a collection of documents. Topic modeling is a text mining tool frequently used for discovering hidden semantic structures in body text. One of the datasets that can be used for this type of project is the New York Times News dataset.

If you need implementation for any of the topics mentioned above or assignment help on any of its variants, feel free to contact us.

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