Analyze data Pre process by Sort data, Filter data, Group data, clean data etc.

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Learning Goal: I’m working on a machine learning project and need an explanation and answer to help me learn.Project ObjectivesThe aim of this project is to provide the students with the opportunity to: Work with real-life datasets to train a Machine Learning (ML) model
Demonstrate the competencies and skills needed to perform fundamental Machine Learning
techniques (PreProcessing, Curve Fitting, Classification, Clustering and Deep Learning) Write and present a brief executive report of findings and recommendations
Project DescriptionYou are required to work to analyze real time data using Python. In this project you will read thereal-life dataset from national or international data source on suitable domain. Set 7 objectives to analyzefor the chosen data set using Python Pandas, Numpy, SciLearn etc. library. Write a well-structured reportthat contains executive summary and recommendations on your findings.Skills to be demonstrated:The selected dataset and derived ML Model challenge the student to demonstrate the following skills: Ability to read data from external files and store data in a Pandas Data Frame
Analyze data Pre process by Sort data, Filter data, Group data, clean data etc.
Perform fundamental Machine Learning (Supervised or Unsupervised) techniques (Curve Fitting,
Classification, Clustering and Deep Learning) Train, optimize and test your model.
Visualize by appropriate plotting/charting
Data Sources:encouraged to select an appropriate dataset from any of the open data projects including: USA Open Data Project: https://www.data.gov
European Open Data Project: http://data.europa.eu/euodp/en/home
The dimensions of an appropriate dataset are at least 1000 rows by 10-20 “relevant” columnsProject DeliverablesPROJECT REPORT TEMPLATECreate and submit a well-structured MS Word Report including all jupyter Notebook (Python code). Thereport must include the following sections: [70 marks]1. Project introduction [3 marks]Describe the project aim and the chosen dataset in terms of source, columns.2. Analysis Questions: (7 marks)You need to define 7 questions for the chosen dataset in order to analyze and visualize its data.These questions should be relevant, by providing useful information that may help in takingdecisions.3. Data Acquisition and Preprocessing [18 marks]Provide the Python code to read the dataset, preprocess (encoding, conditions, sorting, groupingnormalizing etc.) and clean unnecessary data with clear explanation of why and which cleaningneeded for the selected data set.4. ML Modelling Analysis [24 marks]Provide the Python code that performs the Machine Learning for the chosen ML solution the usageof, Regression, Forest Tree, KNN, SVM, K Means, PCA etc. Vs. Deep Learning. Provide the fullworking code with the quality assessment results (RMS, Rsq, Confusion Matrix, Roc. etc.) foreach used model with a clear explanation on the obtained results.5. Data Optimization and Visualization [9 marks]Provide the Python code to optimize and use 3 types of Python chart, with suitable titles and colorsto visualize the data for suitable analysis. Provide the code that shows the model visualization.6. Executive Summary [7 marks]Write an executive summary that provides a clear snapshot of the project and highlights all thekey findings and give a clear recommendation with justifications.7. References [2 Marks]Include all the external references that you might have used. APA/MLA referencing style may beusedProject Marks
Requirements: 3000

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