Project on Stock Prediction using sentimental analysis

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Reference no: EM132248696

Assignment -

Project on Stock Prediction using sentimental analysis (Using R or Python Programming Language).

CAPSTONE PROJECT: FINAL SUBMISSION

Project Title

Project Description

Final deployment of the project

List your major learnings from this project.

Major tools used

Final number of data points used in the project

Final number of data points used for training

Final number of data points used for testing / validation

Mention cross validation techniques used, if any

Data sourced from

Methods used to source the data

Challenges faced to source data

Describe the efforts spent and challenges overcome during data preparation, and the steps used.

Outline the major steps used to implement the project.

List the major machine learning techniques used / implemented in your project.

Mention the major challenges faced and overcome during model creation.

List the techniques used to explore the data.

List the outcome of data exploration.

List any unsupervised learning technique used during data exploration.

How many features were finally identified to create the model.

Describe your Feature Engineering steps, if any.

List and describe any feature reduction techniques used.

Project Proposal -

1. Title of the Project

2. Brief on the project: A brief proposal that describes the idea for a project, the work you intend to perform. In particular, it should identify the project type, the problem you plan to address, the motivation for why you find the problem important or interesting, any previous work you already know about, and a rough tentative approach to solving the problem.

3. Deliverables of the project: A high-level description of the general approach you will use to address the problem. This should include how you will evaluate and what evidence you are planning to gather (e.g. how you can solve the problem through experiments on data)

  • List of questions your model/problem are designed to answer.
  • Details of the model , important findings, expecting observations and outcome.

Note - All codes line should e commented for explanation to allow the user provide write up independent documentation on the codes.

Attachment:- Assignment Files.rar

Reference no: EM132248696

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Reviews

len2248696

3/5/2019 10:08:13 PM

Instructions: "I have mentioned that it is either R Programming or Python". Whichever language that will be used (either R or Python, all codes line should e commented for explanation to allow the user provide write up independent documentation on the codes). Attached are the requirements.

len2248696

3/5/2019 10:08:08 PM

Resources - Data set source: The proposal should also discuss sources of real-world data for your chosen application or how you plan to obtain real-world data. URL of the data set also should include in the report. Soft ware: Software you will choose to solve the problem. References: Include 1-3 relevant papers which already discussed same/similar problems. Team Members: Names, E-mail Ids and Mob of the people working on the project. Maximum team size is four participants. Refer Data Science Process Doc – if deadline is missed no weightage will be given and it will entail loss of grade for the group. All milestones should be submitted online as per date indicated by the group.

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