15+ Machine Learning Projects for Resume with Source Code

Machine learning projects for resume that you can add to show how your machine learning skills and experiences fit into the ML job role you're applying for.

15+ Machine Learning Projects for Resume with Source Code
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Sending out the exact old traditional style data science or machine learning resume might not be doing any favours in your machine learning job search. With cut-throat competition in the industry for high-paying machine learning jobs, a boring cookie-cutter resume might not just be enough. What if we told you there is a simple addition to your machine learning engineer resume to increase your chances of landing a lucrative ML engineer job. 

You would add it in a jiffy, right?

Well, yes, there is. All you need to do is highlight different types of machine learning projects on your resume. 

The best way to showcase you have the required machine learning skills is to highlight how you’ve mastered those skills practically.


Classification Projects on Machine Learning for Beginners - 1

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Machine Learning Projects for Resume - A Must-Have to Get Hired in 2023

Machine Learning and Data Science have been on the rise in the latter part of the last decade. Thanks to innovation and research in machine learning algorithms, we can seek knowledge and learn from insights that hide in the data. Data Engineers, Data Scientists, Data Architects have become significant job titles in the market, and the opportunities keep soaring.



Machine Learning Trends in Recent Years



Deep Learning Trends in Recent Years

With the global machine learning job market projected to be worth $31 billion by the end of 2024 and fierce competition in the industry, a machine learning project portfolio is a must-have. We’ve compiled a list of machine learning projects for a resume to help engineering students or anyone wanting to pursue a machine learning career stand out like GitHub Copilot in the interview. A strong machine learning resume includes different types of machine learning projects. What’s better, we have categorised them into different types, so you can include one project of each type to upgrade your resume with a versatile machine learning skillset for your next ML job interview. Every domain of machine learning presents its challenges and solutions. Hence, having diverse types of machine learning projects for your resume helps recruiters understand your problem-solving approach to various business problems.

A typical machine learning project involves data collection, data cleaning, data transformation, feature extraction, model evaluation approaches to find the best model fitting and hyper tuning parameters for efficiency. Building an ML project from scratch ensures understanding of every step in the machine learning project lifecycle

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Machine Learning Projects for Resume - The Different Types to Have on Your CV

The ML project types listed below are not exhaustive. Still, they cover diverse types of machine learning projects that can add value to a resume and also one should get hands-on practice before appearing for any data science or machine learning job interview.

machine learning projects for resume

Classification refers to labelling groups of data into known categories or classes. Having ML projects on classification listed on your resume help hiring managers to understand your skills on how to tackle any classification problem end-to-end and select the suitable classification machine learning algorithm. Quite similar to classification is clustering but with the minor difference of working with unlabelled data. Clustering defines the process of grouping together identical objects into individual clusters. So, you can add both classification and clustering related machine learning projects to your resume. 

Predictive modelling often uses historical data to learn and predict the likelihood of an event in the future. Historical data provides insights and patterns for making valuable business predictions—for example, predicting customer churn for an organisation in the next 30 days. Having prediction machine learning projects will help hiring managers to understand how the predictions made by an ML model you built can help organisations take action on a product or a service.

Working on hands-on ML projects that employ machine learning algorithms like OpenCV, VGG, ResNet to make sense of real-world objects and environments will show your abilities on how you handle diverse computer vision tasks using machine learning. Some examples of such problems include real-time fruit detection, face recognition, self-driving cars, etc. 

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NLP helps computers understand, analyse, and process human language to derive meaningful insights from it. Recognising handwritten letters, speech recognition, text summarization, chatbots, etc, are some projects that you can build to showcase your NLP skills. NLP projects are a treasured addition to your arsenal of machine learning skills as they help highlight your skills in really digging into unstructured data for real-time data-driven decision making.

Deep learning is a subset of machine learning and one of the most hyped machine learning techniques today. Add deep learning and neural network projects to your resume if you want to showcase your advanced machine learning skills.  Adding deep learning projects to your resume is not a must-have if you’re applying for entry-level machine learning job roles, but they are good to have. 

Time series analysis and forecasting is a crucial part of machine learning that engineering students often neglect. Adding machine learning projects from time-series data is an important machine learning skill to have on your resume. Usually, the time element in the data has valuable information for a machine learning model to glean insights, but at times, it could lead to insights that might not be real. Showcasing time-series projects on your resume will highlight your ability to identify the challenges associated with working with time series data and how you tackle those challenges before it’s too late.

Top 30 Machine Learning Projects for Beginners 

Machine Learning Project Ideas for Resume

Let's delve into the different types of ML project ideas in more detail.

1) Machine Learning Projects for Resume on Classification

Classification in machine learning is a technique that classifies data into selected classes or labels. Syntactically or semantically similar data form one particular class. The classes are referred to as collections, labels or targets as well. A typical classification problem is to identify the class for a given data point or instance.  

The principle behind classification problems is to feed large amounts of data to the model and check for prediction accuracy using supervised learning. The idea is to try multiple models and assess the best-suited algorithm for the problem. Since real-world problems are peculiar and characteristic, it is imperative to check for different models before deciding which machine learning model best fits a given use case. 

Machine Learning Project Ideas for Classification Problems

Sentiment Analysis ML Project for Product Reviews 

Sentiment Analysis is the process of identifying the emotions/sentiment in a text. Companies commonly use it to infer social media reviews, customer response and brand reputation. Sentiment analysis segregates the core of the sentiments into four main categories. 

  • Polarity is the tonality of the text—Ex, negative, positive or neutral.

  • Emotion signifies happiness, sadness, anger or confusion. 

  • Urgency means the graveness and criticality of the text, namely urgent or not urgent.

  • Intention infers whether a customer is interested or not interested.

Pairwise Ranking and Sentiment Analysis of Customer Reviews

The data set for the project contains over 1600 product reviews for medical products, which have been labelled as informative and non-informative. The project’s goal is to perform sentiment analysis on the reviews and rank them in their order of relevance. We start with preprocessing and cleaning the data which is sent to the feature extraction module. After the features are collected and vectorised, we proceed with the classification. Random Forest algorithm is used and performs reasonably well with an accuracy of 85 per cent and above. Finally, pair-wise ranking is done for each review against every other review.

E-Commerce Review Sentiment Analysis Project with Guided Videos

Building Recommender Systems

Recommender systems suggest similar items, places, movies, objects based on a person’s personality, preferences, and likings. Behind the scenes, it groups people with similar tastes together and recommends items from their collective repertoire. Recommender systems are widespread in the industry, with massive applications in eCommerce(Amazon), media(Netflix), and financial institutions (PwC). Content-based filtering and collaborative filtering are the most common techniques employed in the implementation of recommender systems.

Evolution of Machine Learning Applications in Finance : From Theory to Practice

Music Recommendation System on KKbox Dataset

The project aims at predicting if a user will listen to a song again in a period.

KKbox provides a dataset for the project in user-song pairs and the first recorded listening time, along with song and user details. Outliers in the dataset are dropped, and null values are imputed.  The XgBoost algorithm is used to predict the chance of relistening with the highest accuracy. 

Music Recommendation System Project with Guided Videos 

Spam Email Filtering

Spam mail classification labels suspected emails as spam and stop the mails from reaching the mailbox. It checks for the mail content, specific signatures and suspicious patterns to learn about spam mails. There are many techniques available to filter out spam emails -

  • Content-Based Filtering

Content-Based Filtering creates automatic filtering rules by analysing words, the occurrence of specific words and phrases in the mail. 

  • Rule-Based Filtering

Rule-Based Filtering uses already created rules to score the message in the text by comparing the regular expression. If a text matches a certain number of threshold rules, it is tagged and spam, thus dropped. The rules are updated periodically to keep up with the variety and novelty in spam messages.

  • Case-Based Filtering

Case-Based Filtering is among the more popular filtering techniques where spam and non-spam emails are added to a dataset. The dataset goes through the preprocessing stage, and all the emails are converted to two vector classes, spam and nonspam. Learning algorithms are applied to the vectors to classify them as spam and non-spam emails. And finally, testing for new mails occurs on the model. 

  • Adaptive Spam Filtering

Adaptive Spam Filtering classifies spam emails into various classes. The complete email dataset is divided into groups with emblematic signatures—the algorithm checks for similarities between the incoming mails and the groups and classifies the mails accordingly. 

You can use the day to day email exchanges that are tagged as spam and not spam as the dataset for this ML project idea. The data goes through preprocessing steps like stop words removal and vectorisation, which return the data set in a vector form ready for modelling. The model trains using Logistic regression with an accuracy upwards of 90 per cent. An output class of 1 means that the mail is spam where zero signifies not-spam and one as spam.  Another popular classification algorithm called Naive Bayes Classifier also provides good accuracy. 

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2) Machine Learning Projects for Resume on Prediction 

A prediction problem in machine learning is the most common, at par in occurrence with classification problems. Predictions take historical data and find the insights and trends hidden in the dataset.  The larger the dataset we have for training, the better and more accurate the prediction algorithm becomes. The current and historical data is taken to build a model that can predict events or trends in the future. The predictions can range from the potential risk of a credit card issue request to calculating the stock prices for a multinational company.

Machine Learning Project Ideas on Prediction Problems 

Sales Forecasting

Forecasting future sales depends on many factors like past sales, seasonal offers, holidays and festivals etc. Future sales also dictate staff requirements and stocking product inventory for future needs. Autoencoders and multivariate models can make a good fit for forecasting prediction problems where time is an added constraint. 

Rossmann Store Sales Prediction Project

The dataset contains historical data from more than 1000 Rossmann drug stores, including customer id, sales, store, state holidays, etc. Missing data points are imputed, and outliers get removed. Data is converted into numerical form by using one-hot encoding for easier manipulation. Stochastic Gradient Descent and Decision Tree regressor algorithms are mainly used in the model. 

Project to Forecast Future Sales of Rossman Store with Guided Video

We tend to look at the weather report multiple times in our daily life. Predicting rainfall is of utmost importance to industries that depend on rains like agriculture. Weather predictions are relatively challenging and better done using Deep Learning algorithms. Even so, traditional ensemble models can offer outstanding results with the need for high resources. 

The project’s data set is featured at Kaggle with information on the date, the average temperature on land and sea, minimum and maximum temperature on land and sea. The previous value replaces null values in the dataset, and date entries are converted to a DateTime object. The Zero-differentiated ARIMA model is used for prediction as, along with being a prediction problem, weather forecasting is also a time series problem. Finally, the accuracy is measured by Akaike Information Criterion. 

Customer churn is the behaviour of customers to stop using an organisation’s products or services. Customer churn rate is the rate of people who discontinue paid services in a particular interval of time. Churn is bad for companies as they lose revenue. Churn prediction finds applications in telecom, music and movie streaming services or other subscription-based services. Churn also signifies the health and reputation in the market for a company.

Customer Churn Prediction Analysis for Bank Records

The dataset from the bank records stores customer name, credit score, geography, balance, tenure, gender, etc. Preprocessing, imputing and label encoding are the next steps that occur. The dataset goes through feature extraction at this stage, eliminating less essential fields, making the dataset manageable and more consistent. The Light Gradient Boost Machine or LGBM algorithm provides maximum accuracy and is preferred for this project. Being lightweight, it is suitable in the big production setting of a bank.

Customer Churn Prediction Project with Guided Videos 

3) Machine Learning Projects for Resume on Computer Vision  

Computer Vision combines machine learning with image/vision analysis to enable systems to infer insights from videos and images. For a computer, it becomes quite a challenge to interpret pictures and distinguish the features. While as humans, we have evolved over a long time with our vision as a central characteristic. For humans, using vision to recognise objects around us is almost second nature. Computer Vision offers the possibility for computers to develop the vision to assimilate and comprehend the world around them. 

The main principle involved in computer vision is to break the image into pixels. Pixels are the most fundamental constituents of an image. By recognising the pattern in the pixel pool, computers begin the task of image identification. Equally important is what features we extract from these pixels and how we construct the learning model.

Computer Vision Techniques

Object Detection is the identification of objects in an image. These objects can be any person, thing, animal, or place but need distinctive features that the model uses to recognise and detect the subjects in the photos. Object detection happens through localisation, where a bounding box outlines the object. The object comprises many pixels, and those pixels belong to the same object class. Object detection is used in google photos, where google detects faces from our library of images.

Object Tracking refers to following the path of a particular object in a situation or environment. Stacked Auto Encoders (SAE) and Convolutional Neural Network  Surveillance is an ideal example of object tracking. 

Image Classification is tagging images under a class holding similar photos. An example of image classification is the annoying ‘Not a Robot’ authentication that forces one to select all the traffic lights in the image. 

Image Segmentation

Image segmentation aims to break the image into partitions or segments so that it’s easier to analyse and process the whole picture. There are two types of image segmentation possible, listed as follows:

  • Instance Segmentation - It recognises each object of the same type as a new object. So an image of three elephants would be categorised into three separate elephant classes, namely, elephant1, elephant2 and elephant3.

  • Semantic Segmentation - It understands the semantics in the pixels and labels semantically similar objects in the same class. Considering the elephant example from above, pixels in the image of three elephants will get tagged under only one elephant class, namely elephant.

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Machine Learning Project Ideas on Computer Vision

 

Face Recognition

Face recognition is a non-trivial computer vision problem that recognises faces and clusters them under appropriate classes. Face recognition finds uses in mobile phone applications, surveillance, photo tagging applications, google lens, etc. OpenCV is the most popular library that helps with building models for face recognition. 

Face Recognition System in Python using FaceNet

The dataset for the project is a video from the famous sitcom show called Friends. Frames per second from the video are extracted to form the dataset in which we need to recognise the cast’s faces. A total of 35 images, with seven images for each character, are collected. Haar Cascade Object is used for face detection and extraction, while Convolution Neural Network is used for model training. 

Face Recognition Project using Facenet with Guided Videos

Optical character recognition is the technique of identifying the letters and digits in a handwritten document or bill. It extracts the relevant information from the documents and records it in the database. Since handwritings come in numerous styles, OCR needs extensive training and fine-tuning of parameters. 

Building an OCR System from Scratch 

Building OCR in Python using YOLO and Tesseract

The dataset for the project is created using the Labellmg tool in python to label all the invoices present. After the labelling, we proceed with the YOLOv4 ( you only look once ) algorithm to detect the invoice number, date and total bill amount. Next, Tesseract is used to read/predict text from the detected fields.  We also use image augmentation to expand the dataset to a considerable size if the dataset is small. 

OCR Project Built from Scratch with Guided Videos 

Image restoration is the reconstruction of old images to make them new-like with optimum quality and features. It takes into consideration both spatial information and frequency to replace missing values in a snap. 

4) Machine Learning Projects for Resume on NLP 

Natural Language Processing is part of machine learning that involves understanding and processing human language, text, and spoken.  Here is a list of prevalent NLP tasks that will help in getting a sense of its wide array of applications:

  • Speech Recognition

  • Part of Speech Tagging

  • Word Sense Disambiguation

  • Named Entity Recognition

  • Coreference Resolution

  • Sentiment Analysis

  • Natural Language Generation 

NLP Techniques

Natural Language processing uses two effective techniques which differ in their approach to analysing language; they are namely:

  • The Syntactical Analysis makes use of grammar to identify and analyse the natural language. It checks for sentence structure, the relationship between words and rules of grammar. 
    • Remove Punctuation Punctuations clutter the data with useless tokens and don't add to the model efficiency. It's best practice to remove them beforehand. 
    • Tokenisation is the breaking of sentences into smaller parts that can be either words or combination words. It makes data processing easier and uniform across the whole dataset.
    • Lemmatisation is converting words to their most basic form called Lemma. The lemma replaces every other form of the word. For example, learning, learned, learnt, learnable shall be replaced with learning.
    • Stemming is the process of dropping the beginnings and ends of words depending on their prefix and suffix. 
    • Part of Speech Tagging labels tokens as a verb, adverb, adjective, noun etc., based on the grammatical vocabulary. It helps discern the difference between the noun and adjective forms of the same word if a comment has different meanings. For example, the word sense signifies the five senses and the act of perceiving. 
    • Stop Words Removal focuses on deleting all the common stop words like a, an, the, and, like, just that don't add to the concrete meaning of the text.
    • Vectorisation or Bag of Words is the process of counting the occurrences of individual words in a text. The count of each word helps in understanding how important the word is to the whole subtext.
  • The Semantical Analysis uses the meaning of words instead of syntax to process sentences. It starts with the meaning of each word, then moves on to the meaning of a group of words and finally the meaning of the whole subtext. 
    • Word Sense Disambiguation identifies different forms/meanings of the exact words depending upon the context of its use and neighbouring terms.
    • Word Relationship Extraction tried to infer the relationships between different words in a sentence like a place, subject, object etc.

Machine Learning Project Ideas on NLP

Build a Chatbot

Chatbots are NLP applications that enable us to query details and raise grievances in natural language to receive relevant information. Chatbots are prevalent in the customer service industry, where setting up call centres is cumbersome and not budget-friendly.  An example is the amazon chatbot that helps customers with order information, order cancellation etc. 

Natural Language Processing Chatbot using NLTK

The dataset is conversations from a leave enquiry and application system for the organisation. The textual data is pre-processed using various NLP techniques like lemmatisation, tokenisation, stemming and stop words removal. The occurrence of each word is counted to create a count vector model called a bag of words. You can use algorithms like Naive Bayes Classifier and Decision Tree for modelling. 

NLP Chatbot Project with Guided Videos

Speech recognition is the ability of a machine to understand human language and respond coherently with appropriate data. Speech recognition finds use in our daily life while we use maps, call a friend or translate language all through our voice. Alexa in Amazon Echo and Siri in Apple iPhones are some of the best examples of speech recognition. 

Topic Modelling

Topic modelling is the inference of main keywords or topics from a large set of data. It measures the frequency of a word in the text and its relationship with neighbouring words to extract succinct information. Typical uses are labelling unstructured data into formatted topics. It can also be used in text summarisation problems with minor tweaks to the model. 

Topic Modelling using K-means Clustering on Customer Reviews 

The customer reviews for the project are sourced from Twitter for a particular company.

Data goes through many layers of preprocessing as the twitter reviews are unfiltered and raw. Tokenisation and vectorisation are performed using TD-IDF and count vectoriser. Model training is done using k-means clustering, an unsupervised learning algorithm. The final result is clusters of tweets with different classes that signify the dominant topic in the cluster. 

Topic Modelling for Customer Reviews ML Project with Source Code 

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5) Deep Learning and Neural Networks Projects for Resume

Deep Learning aims at mimicking and simulating human thought patterns by using complex and layered structures called Neural Networks. In simple terms, Deep learning is multiple Artificial Neural Networks connected. Neural networks can accomplish clustering, classification and regression with greater efficiency than traditional machine learning algorithms. 

Deep Learning eliminates the feature extraction process and skips over this step essential to all the traditional machine learning algorithms. These classic algorithms, called flat algorithms, cannot use data without preprocessing or feature extraction. Feature extraction is a detailed and involved process that needs expertise in the problem domain and patience in refining it over time. Deep Learning straight away discards this step and moves on with raw data. Deep Learning can learn and model the problem satisfactorily upon many iterations by tuning the weights using loss functions. 

A brief look at the architecture of a deep learning model

  • Nodes - A neural network is a collection of primary cells called Nodes. A Node stores arithmetic values like 0.4,2.21 etc

  • Weights are the branches that connect two nodes. They represent a number that keeps changing over the training time. The process of starting from a set of random weights to arriving with specific values that fits the input data is called Learning.

  • Loss Function defines the difference between the prediction vector obtained from the Neural network and the actual output vector—the lesser the loss function value, the better the model. 

  • Back Propagation of Errors pushes the errors back towards the input layer. In the process, it keeps updating the weights in each hidden layer. The principle behind this is that the total error gradient in the output layer is the sum of individual error gradients at each point in the network. 

  • Gradient Descent is when the weights are tuned using the derivative of the loss function to improve the network. The idea is to bring the weights to a value that spawns the most accurate prediction. 

  • The nonlinear activation function is applied to the dot product of the previous hidden layer vector and weights connecting the two participating layers. 

  • A feature vector is the input vector that goes into the Neural Network through the input layer. It contains a vectorised form of the input.

  • Prediction vector is the vector form of the output that the neural network produces.

  • Input and Output layers - Input and output layers are a neural network’s first and last layers. 

    • Input Layer is a set of nodes that represent a data point in vector form. For example, for image recognition models, the input layer would be the vectorised version of the image pixels. 

    • Output Layer denotes the result of the Neural Network. It is again a set of nodes quite like the input layer, but these individual nodes represent the output classes of the problem. For example, in the image recognition problem, the output layer would be nodes corresponding to objects in the image like cars, sheep, women etc.

  • Hidden Layers are the layers sandwiched between the input and the output layer. All the computations ( like weights tuning ) happen among these layers.

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Deep Learning and Neural Network Project Ideas for Resume

Self-Driving Autonomous Cars

Autonomous-driving cars can navigate through traffic and control acceleration and speed depending on their environment. Perception, Localisation, Planning, control are the four central ideas in self-driving cars. 

  • Perception is figuring out the environment and obstacles. 

  • Planning is the trajectory from point A to point B

  • Localisation is identifying the current location in the world.

  • Control relates to steering angle and acceleration. 

 

  • Natural Language Translation using Deep Learning 

Natural Language Translation using Deep Learning 

Language translation is extremely important in international trade, discourses, education and media where two parties interact without any common language. It translates text or speech from one language to another.

For example- Google translate is a google cloud application that offers text translation into various languages. It uses Transalatron to develop the learning model. Neural Nets used are LSTMs and sequenced RNNs with an encoder-decoder model. 

Credit Card Anomaly Detection using Autoencoders 

The project aims at detecting fraudulent credit card transactions so the system can curb them and charge the customer of only the actual transactions. The dataset contains records of credit card transactions that are legal and fraudulent which have been passed through PCA (principal component analysis)analysis to change the data fields into numbers. We also have transaction amount, the time difference between consecutive transactions, and fraudulent transaction each unique credit card. Neural networks and autoencoders are used in conjunction with each other for modelling. And finally, the accuracy of the model is measured using Mean Squared Error (MSE) with the ggplot2 package. 

Credit Card Anomaly Detection Project with Guided Videos

6) Machine Learning Projects for Resume on Time Series Data

Time Series data helps predict an object’s behaviour compared to its older state in time. Time series is a dataset of continuous and periodic observations of the time instances attached to the data itself. Time Series finds use in many prediction scenarios like weather prediction, prediction for the price of an item, sales prediction, etc. It is much like prediction but with an added time constraint or feature, making it an altogether different problem of time-series forecasting. Generally more complex than traditional prediction projects.

Datatypes in Time Series

  • Time Series Data are observations recorded at different instances in time for a set period. 

  • Cross-Sectional Data

Data values of more than one variable are gathered at the same time. Thus, freezing or capturing the state of a system as a single entity in time. That is why the word cross-section comes into play, which implies a time-print of the model.

  • Pooled Data is the mixture of time-series data and cross-sectional data. 

Types of Time Series Modelling

Time series forecasting further divides into two subcategories based on the number of variables in the model, which are as follows:

Univariate Time Series Forecasting is when only one other forecasting variable is present in the model apart from time. For example, in a sales prediction model, the number of sales is the only one that will vary with time.

Multivariate Time Series Forecasting model is one where multiple variables are changing with time. Naturally so, the forecasting depends on these variables as well. For example, the temperature during the day depends on many variables like rainfall, wind, overcast etc. So essentially, a model to predict the temperature would be a candidate for multivariate time series forecasting.  

Overview of Seasonality and Autocorrelation in Time Series Data

  • Autocorrelation defines the similarity between a time series and its lagged version, showing the relationship between past and present values. Autocorrelation is also called lagged correlation or serial correlation. It ranges between the value of -1 to 1. 

  • Seasonality signifies periodic fluctuations in the graph of time series. Quite simply, it means that the data in the sequence repeats after a specific time called the period. Seasonality is generally calculated over one financial year.

Time Series Analysis and Forecasting Techniques 

  • ARIMA or Auto-Regressive Integrated Moving Average combines three models, i.e. ‘AR’, ‘MA’ and ‘I.’

    • AR shows the evolving variable of interest regressing over its initial values.  

    • MA shows that the regression error is the linear combination of error term values at previous instances. 

    • I shows that the data values are replaced by differences in their values from older values. 

  • Moving Average is so-called because each data point averages the data values before and after in the time series and creates a new time series. Moving Average highlights trends and trends cycle. It is ideal for univariate time series.

  • Exponential Smoothing creates the new time series by average the weight values from the current time series. A Datapoint in a time series has less weight if it's older in time compared to a recent data point.  The theory being that recent data has more chance of reoccurring again.

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Machine Learning Project Ideas for Resume on Time Series Data

Weather Forecasting 

Weather forecasting is a complex time series problem that uses past weather data and related parameters like wind pressure, overcast, wind speed etc., into account while forecasting future weather. 

The dataset contains the average temperature recorded in 2000 stations over Helsinki for some time. The SARIMA or Seasonal ARIMA model is used to model, and the Root Mean Squared value is used to check the accuracy.

Sales prediction in a company is again a time series problem that considers the month of the year, holidays around and seasons in predicting future sales. The sales data show a cyclic trend in data that repeat every year. The dataset contains product information such as item id, item weight, type of the item, item MRP, etc. The dataset undergoes imputing of null values and one hot encoding. Outliers are identified with boxplot and deleted accordingly. Gradient boost tree and xgboost algorithms are applied for modelling, but the most efficient algorithm turns out to be a neural net with MLPRegressor. 

Project on Bigmart Sales Prediction with Guided Video tutorials

Stock Prediction 

Stock Market prediction depends on the historical stock records, geopolitical environment and company performance in recent times. It is a complicated prediction problem that involves time series along with deep learning.

The data is taken from the EU stock market with fields like the German DAX stock index, UK stock index, etc. We extract the trend and seasonality in the dataset and identify correlations and autocorrelations. Vector Autoregression (VAR) is used for modelling with good accuracy among other algorithms like ARIMA and LSTM.

Time Series Project on Stock Market with Source Code and Explanatory Videos

How to List Machine Learning Projects on Resume?

If you are a recent college graduate or in the final year of graduation, you know how difficult it is to create a data science or machine learning resume without prior work experience. However, adding diverse machine learning projects mentioned above can definitely add credibility to your resume.

It is essential to treat the various types of machine learning problems discussed above as a general guide since each project is unique and needs a precise approach. One can start by learning one project in each category and proceed from there. It is crucial to take note of learnings from each project and list them in the resume. 

Here’s a recommended list of blogs on different types of project ideas for further exploration and reading -

FAQs on Machine Learning Projects for Resume

1) How do you put machine learning projects on your resume?

Machine Learning projects should be brief and to the point on the resume. One can briefly discuss the dataset, model training, libraries used and accuracy by mentioning only the crucial points. 

2) Are Machine Learning projects good for a resume?

Indeed, machine learning projects are great additions to one’s resume. Machine learning is a burgeoning field and adding ML projects to the resume opens up job more opportunities. Candidates who wish to make a career in Machine Learning or Deep Learning need to build a versatile portfolio of ML projects for the resume.

3) Can one do Machine Learning projects in an Internship?

Yes, one can do machine learning projects in internships. In fact, during internships, one learns to build and deploy machine learning projects in real-time. It is an ideal environment to expand one’s experience and knowledge. But it is equally essential to be able to land an internship in the first place. It is best to start learning and practising machine learning projects at our own pace and slowly build an internship resume. By enlisting some prior understanding in machine learning projects, one can increase their chances of landing a machine learning internship. 

4) What projects can I do with Machine learning?

With Machine Learning, one can do many projects depending on the project type and theme.  A good strategy would be to pick one project from each category, as discussed above, for the resume. Face Recognition Project, Sales Prediction projects, Recommendation System Projects, Building a chatbot using NLTk, Spam mail detection project etc., are good choices to get started with gaining hands-on exposure to diverse kinds of problems.

5) How does one write a Data Science project for a resume? 

  • A data science project for a resume should have a brief introduction followed by a one-line explanation about the dataset and data-cleaning techniques involved. 

  • Following that, one should write about the models used and the model that produced maximum accuracy.  

  • It is crucial to remember not to be long-winded in describing the project and mention the significant points. 

  • In the end, you can conclude by remarking about the learnings obtained during the project and key takeaways.



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