777 skills found · Page 23 of 26
Warishayat / Pandas Numpy Matplotlib SeabornThis project explores data analysis and visualization using NumPy, Pandas, Matplotlib, and Seaborn. It provides practical tutorials on data manipulation, statistical analysis, and creating insightful visualizations. Ideal for learners looking to master these essential Python libraries for data science and explore real-world datasets effectively.
SaijyotiTripathy / Heart Attack Analysis And PredictionTo explain and identify the problem and resolve medical objectives, different data science techniques, which interpret the medical goals, have been implemented to diagnose heart disease. A suitable machine learning algorithm called Logistic Regression is preferred for the training and implementation in python for developing and evolving the predictive model. This algorithm executed on the model will help medical experts to predict and diagnose heart attacks in the patient dataset. Exploratory Data Analysis is performed using python libraries such as Matplotlib and Seaborn to visualize the correlation between features.
avannaldas / QuickViewQuickView is a python package for a quick glance of the dataset, just pass the pandas dataframe. It gives some useful summary and plots with just one line of code. All the summary that it outputs is made available through member variables. It is built using matplotlib.
HxCodeWarrior / ML Study零开始学习机器学习，一起手搓大模型。本项目需要参与者一定的数学基础：线性代数、概率论；需要一定的编程基础：python基础知识、numpy库基础、pandas库基础、matplotlib库基础。就算没有这些基础我们也可以在参与的过程中，不断学习，不过作者还是建议先掌握基础，然后再参与学习此项目。参与学习此项目，为我们手搓大模型打下坚实基础。加油吧各位Inventor！！！
nishikantgurav / Heart Disease Prediction Using Neural NetworksThis project will focus on predicting heart disease using neural networks. Based on attributes such as blood pressure, cholestoral levels, heart rate, and other characteristic attributes, patients will be classified according to varying degrees of coronary artery disease. This project will utilize a dataset of 303 patients and distributed by the UCI Machine Learning Repository. Machine learning and artificial intelligence is going to have a dramatic impact on the health field; as a result, familiarizing yourself with the data processing techniques appropriate for numerical health data and the most widely used algorithms for classification tasks is an incredibly valuable use of your time! In this tutorial, we will do exactly that. We will be using some common Python libraries, such as pandas, numpy, and matplotlib. Furthermore, for the machine learning side of this project, we will be using sklearn and keras.
MariamGado0 / Starbucks Capstone Project ML Udacity Aws# Starbucks Promotions Project ### This project is the Capstone Project of Udacity's Machine Learning Engineering Nanodegree program.    ## Problem Statement This data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. Once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free). Some users might not receive any offer during certain weeks. Not all users receive the same offer, and that is the challenge to solve with this data set. The task is to combine transaction, demographic and offer data to determine which demographic groups respond best to which offer type. This data set is a simplified version of the real Starbucks app because the underlying simulator only has one product whereas Starbucks actually sells dozens of products. Starbucks collects the customer data to understand their behaviour on the rewards and offers sent via the mobile-app. Once every few days, Starbucks sends the personalised offers to its customers. These customers can respond positively/negatively/neutrally. A key thing to note is that not all the customers receive the same offer. The task of this project is to combine transaction, demographic and offer data of the past (which is already provided) to determine which demographic groups respond best to which offer types. In order to develop this project, we needed to use some tools, packages, systems and services that could help us achieve our goals. #### Libraries First of all, we used **Python** to write our scripts not only for algorithm training and serving but also for the orchestration of the whole process. Important packages within this environment are listed below: This project is developed in Python 3.6. You will need install some libraries in order to run the code. Libraries are: * `pandas` so we could work with tabular data in dataframes; * `Ploty` so we could visualize our Dataset; * `matplotlib` for Dataset visualization; * `numpy` so we could easily manipulate arrays and data structures; * `seaborn` and `matplotlib` so we could generate insightful visualizations; * `sklearn` so we could build and develop our model pipeline; * `imblearn` so we could apply SMOTE to our training data; * `xgboost` so we could have our main classifier; * `sagemaker` so we could easily interact with AWS. * `json` for reading our Dataset Files. * `boto3` Finally, we used AWS environment in order to launch training jobs, deploy our model and serve predictions. The main services used are also listed below: * __AWS SageMaker__: training, hyperparameter tuning and endpoint serving; * __Amazon S3__: saving our data and model artifacts; ## Files Descriptions This project is structured as follows: #### 01. Proposal Project proposal documentation. #### 02. Data_Cleaning_[Dataset] Folder to perform data preparation and Dataset Cleaning and Prepare the Final Data for Further using in model algorithms. #### 03. Pre-processing Dataset Visualization Folder to perform final Pre-processing Dataset to be used in Visualization and exploration. #### 04. Dataset_Visualization Folder to perform Visualizations for the Pre-processed Dataset. #### 06. ORG_Starbucks_Capstone_Project.ipynb Jupyter notebook file that deploy final model and create an endpoint and orchestrates the end-to-end process in AWS SageMaker and also interacts with other services.
InoveAlumnos / Intro Matplotlib PythonMaterial de clase y ejercicios acerca de Matplotlib
epurpur / PythonDataVizRepository for RDS MatPlotLib workshop - Python Data Visualization
abhi3700 / My Learning PythonLearn Python - Excel, Pandas, Matplotlib, Xlwings
chardur / MultipleLinearRegressionPythonMultiple linear regression with Python, numpy, matplotlib, plot in 3d
phoehne / EconoMetricsSome useful python libraries (requires matplotlib and pandas)
nomissbowling / PyobPython PyObject wrapper for C++ matplotlib numpy wx etc
halac123b / Visualize Data From Lidar LD19 Matplotlib PythonNo description available
DEEPI-LAB / Dbscan PythonPython implementation of 'DBSCAN' Algorithm Using only Numpy and Matplotlib
jimioke / GroupstackbarPython package for creating grouped and stacked bar plots using matplotlib
CfKu / Matplotlib Din461matplotlib-din461 - Changes the appearance of a python matplotlib 2D plot in accordance with DIN461
Alicelibinguo / Analyzing Website Landing Page A B Test Results Conducted hypothesis testing in python to find out whether the new website landing page has higher users’ convert rate than old page using Pandas and Matplotlib
Code-for-dream / Python Python数据分析相关的学习笔记（numpy、matplotlib、pandas）
fsilva / Virtual FemtolabPython+wxWindows+matplotlib application to calculate propagation effects in ultrafast pulsed laser beams
itsliterallymonique / Barnsley FernA Python program creating the Barnsley Fern fractal using a scatterplot (matplotlib library)
