Exploring 118 wells of 1 MM+ rows and 29 columns of wireline petrophysical data using the Pandas library. Analysed & Visualised wireline logs petrophysical dataset using - Pandas, Numpy, Matplotlib, Plotly & seaborn libraries Discovered insights of wireline logs quality & interpretation (missing data and imbalance class

Use pandas, Data visualisation libraries(Matplotlib or Seaborn) to establish conclusions about a dataset.

Performed EDA on a Housing Dataset after cleaning it using Pandas and Numpy for Data Manipulation followed by Matplotlib and Seaborn for Data Visualisation

Developed a multivariate multistep time series forecasting model using lagged variables to predict the electricity power consumption for next 7 days. Cleaned the data and performed data exploration and visualisation using matplotlib, seaborn, ggplot2 and created new variables for the date time indexed dataset based on seasonality, time of the day and day of the week etc. Trained the model on various machine learning algorithms including SVR, LR , Random Forest , XGBoost and got a RMSE of approximately 280.

The Titanic: Machine Learning from Disaster competiton. With data being provided of varoius passengers traveling on the ship I have used libraries like numpy,pandas to manipulate , explore and analyze the data and libraries like matplotlib and seaborn to visualise the data. Lastly I have used various machine learning models to make predictions on the formerly cleaned and preprocessed data. Then I used GridSearchCV to optimise the parameters of the various models

Data analysis of Bayern Munich and German players in the 2025 UEFA Champions League, exploring goals, assists, attempts, defensive actions, and goalkeeping stats using Python, Pandas, Matplotlib, and Seaborn for insights and visualisations.

Tutorial on how to use the seaborn library for data visualisation

Complete and Thorough data visualisation tutorial on using Matplotlib and Seaborn from beginning to advance level with in depth customisation and demo of multi variable plots such as Pairplot, Relplot and Catplot

Predicting Heart disease using Machine Learning This notebook looks into using various Python-based ML and Data Science libraries in an attempt to build a Machine Learning model capable of predicting whether or not someone has heart disease based on their medical attributes. We're going to take the following approach: Problem Definition Data Evaluation Features Modelling About Heart Disease Cardiovascular disease or heart disease describes a range of conditions that affect your heart. Diseases under the heart disease umbrella include blood vessel diseases, such as coronary artery disease. From WHO statistics every year 17.9 million dying from heart disease. The medical study says that human life style is the main reason behind this heart problem. Apart from this there are many key factors which warns that the person may/maynot getting chance of heart disease. From the dataset if we create suitable machine learning technique which classify the heart disease more accurately, it is very helpful to the health organisation as well as patients. 1. Problem Defintion In a statement, Given clinical parameters about a patient, can we predict whether or not they have heart disease? 2. Data The original data came from the Cleavland data from the UCI Machine Learning Repository There is also a version of it available on Kaggle. Heart Disease Date 3. Evaluation Target to reach more than 85% If the model scored better than 85% we will select the model 4. Features age: displays the age of the individual. sex: displays the gender of the individual using the following format : 1 = male 0 = female cp (Chest-Pain Type): displays the type of chest-pain experienced by the individual using the following format : 0 = typical angina 1 = atypical angina 2= non — anginal pain 3 = asymptotic trestbps(Resting Blood Pressure): displays the resting blood pressure value of an individual in mmHg (unit) chol(Serum Cholestrol): displays the serum cholesterol in mg/dl (unit) fbs (Fasting Blood Sugar): compares the fasting blood sugar value of an individual with 120mg/dl. If fasting blood sugar > 120mg/dl then : 1 (true) else : 0 (false) restecg (Resting ECG): displays resting electrocardiographic results 0 = normal 1 = having ST-T wave abnormality 2 = left ventricular hyperthrophy thalach(Max Heart Rate Achieved): displays the max heart rate achieved by an individual. exang (Exercise induced angina): 1 = yes 0 = no oldpeak (ST depression induced by exercise relative to rest): displays the value which is an integer or float. slope (Peak exercise ST segment) : 0 = upsloping 1 = flat 2 = downsloping ca (Number of major vessels (0–3) colored by flourosopy): displays the value as integer or float. thal : displays the thalassemia (is an inherited blood disorder that causes your body to have less hemoglobin than normal) : 0 = normal 1 = fixed defect 2 = reversible defect target (Diagnosis of heart disease): Displays whether the individual is suffering from heart disease or not : 0 = absence 1 = present. Preparing the tools We are going to use :- Pandas & Numpy for Data Analysis and Manipulation Matplotlib and Seaborn for Data Visualisation Scikit-Learn for the Modeling building and Reports

Introduction : This data set is a masked data set which is similar to what data analysts at Uber handle. Solving this assignment will give you an idea about how problems are systematically solved using EDA and data visualisation. Business Understanding : You may have some experience of travelling to and from the airport. Have you ever used Uber or any other cab service for this travel? Did you at any time face the problem of cancellation by the driver or non-availability of cars? Well, if these are the problems faced by customers, these very issues also impact the business of Uber. If drivers cancel the request of riders or if cars are unavailable, Uber loses out on its revenue. As an analyst, you decide to address the problem Uber is facing - driver cancellation and non-availability of cars leading to loss of potential revenue. Business Objectives : The aim of analysis is to identify the root cause of the problem (i.e. cancellation and non-availability of cars) and recommend ways to improve the situation. As a result of your analysis, you should be able to present to the client the root cause(s) and possible hypotheses of the problem(s) and recommend ways to improve them. There are six attributes associated with each request made by a customer: 1. Request id: A unique identifier of the request 2. Time of request: The date and time at which the customer made the trip request 3. Drop-off time: The drop-off date and time, in case the trip was completed 4. Pick-up point: The point from which the request was made 5. Driver id: The unique identification number of the driver 6. Status of the request: The final status of the trip, that can be either completed, cancelled by the driver or no cars available Note: For this assignment, only the trips to and from the airport are being considered. Results Expected : 1. Visually identify the most pressing problems for Uber. Hint: Create plots to visualise the frequency of requests that get cancelled or show 'no cars available'; identify the most problematic types of requests (city to airport / airport to city etc.) and the time slots (early mornings, late evenings etc.) using plots. 2. Find out the gap between supply and demand and show the same using plots. a. Find the time slots when the highest gap exists b. Find the types of requests (city-airport or airport-city) for which the gap is the most severe in the identified time slots 3. What do you think is the reason for this issue for the supply-demand gap? Write the answer in less than 100 words. You may accompany the write-up with plot(s). 4. Recommend some ways to resolve the supply-demand gap. IDE : jupyter notebook Language : Python Libraries : Numpy, Pandas, Matplotlib, Seaborn Please do explore the dataset further to your own and see what kind of other insights you can get across various other columns.

Data visualisation using Seaborn & Matplotlib

visualising Mutual fund data using matplotlib,seaborn.

Pet projects demonstrating data wrangling, visualisation and analytical skills in python using libraries like pandas, matplotlib, numpy, scipy, seaborn and plotly.

This project is visualizing different charts from data using pandas and seaborn and matplotlib library. These chart visualisation is helping in gaining insights from the data

In the final project of Data Visualisation with Python, you create impactful visualizations using popular libraries like Matplotlib, Seaborn, and Plotly. Apply skills learned throughout the course to analyze real-world data, effectively communicate insights, and enhance your data visualisation proficiency.

Used NLTK library from text pre-processing ->Data Visualisation with Matplotlib and Seaborn -> used sklearn CountVectorizer and Tfidf transformer for feature extraction from text -> then used Multinomial Naive Bayes algorithm to train the ML model

This project uses the concepts of data science like the EDA(Exploratory Data Analysis) , model development etc with the visualisation and model building libraries like seaborn , matplotlib , scikit learn etc . The dataset was obtained from kaggle

This is a Exploratory Data Analysis Project 📉That makes use of Various Python libraries 📚including Pandas, NumPy, MatplotLib and Seaborn. 💱There are various steps taken into account to perform the analysis including Data Cleaning 🧹and Data Visualisation📊

Data Science is one of the hottest professions of the decade and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Professional Certificate from IBM will help anyone interested in pursuing a career in data science or machine learning develop career-relevant skills and experience. The program consists of 10 online courses that will provide you with the latest job-ready tools and skills, including open source tools and libraries, Python, databases, SQL, data visualisation, data analysis, statistical analysis, predictive modelling, and machine learning algorithms. You’ll learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets. This Professional Certificate has a strong emphasis on applied learning. Except for the first course, all other courses include a series of hands-on labs in the IBM Cloud that will give you practical skills with applicability to real jobs, including: Tools: Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio Libraries: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc. Projects: random album generator, predict housing prices, best classifier model, predicting successful rocket landing, dashboard and interactive mapping

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Exploratory Data Visualisation Using various charts and helper function present in seaborn

Data visualisation using seaborn

This project includes visualisation of weather dataset using python seaborn

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Data Visualization using Matplotlib, Seaborn, and Plotly This repository showcases various data visualization techniques using three popular Python libraries: Matplotlib, Seaborn, and Plotly. It includes examples and code snippets for creating a wide range of plots, from basic charts to interactive visualizations.

Data Visualisation using Matplotlib and Seaborn in Python

Data Visualisation Internship using Matplot and Seaborn libraries

Data Visualisation project using Seaborn, Plotly and Dash