Machine Learning Homework Help
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What is Machine Learning?
Machine learning or AI is a field of software engineering that utilizes different factual methods to let the PC learn all alone by breaking down the information without programming. Machine learning is for the most part utilized in Artificial Intelligence. AI significantly centers around creating PC applications that can get to information and utilize this information to learn without human mediation. The learning procedure begins by watching or with the assistance of information. The primary point is to let PC adapt consequently without the help of people.
Machine learning or AI will utilize calculations that will get information as an info and utilize factual methods to envision the yield while continuing refreshing the yield with the adjustment in the information. The procedure that is utilized in AI is similar to that is information mining and prescient models. In both these procedures, scan the information for design and as needs be alter the program activities. This causes the organizations to make the right business choices by investigating enormous tosses of information. There are various fields that are utilizing AI.
- Prepare for analysis and identify the appropriate data set
- Train the model on prepared data sets for testing
- Choose the right machine learning algorithm
- Run the model and generate findings
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Different Machine Learning Methods
Supervised learning
Classification techniques:
Algorithms used to perform classification include:
- Super Vector Machine (SVM)
- Neural networks
- K-nearest neighbor
- Logical regression
- Bagged decision tree
Regression technique :
The key regression algorithm techniques that are used include:
- Linear model
- Non-linear model
- Regularization
- Neural network
- Stepwise regression
- Bagged decision trees
- Adaptive Neuro-fuzzy learning
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Unsupervised learning
The following techniques are used to explain the data. These include:
Clustering: This is used to carry out exploratory data analysis to find out hidden patterns or data groups. The key applications where this type of technique is used include market research, object recognition, etc.
Dimensionality reduction:
- K-means clustering
- T-Distributed Stochastic Neighbor Embedding
- Principal Component Analysis
- Association rule
Semi-supervised learning
This algorithm will stand between supervised learning and unsupervised learning. This type of learning will pick a few aspects in each of these learning and form into one. This uses labeled and unlabeled data for carrying out training. Enhance your understanding of the subject by availing Machine learning homework help from our experts.
Reinforcement machine learning
The key reinforcement machine learning includes:
- Q-learning
- Monte-Carlo Tree Search
- Temporal Difference (TD)
- Asynchronous Actor-Critic Agents
KIT108 ARTIFICIAL INTELLIGENCE: Sample Machine Learning Homework Help
Synopsis of the task and its context
This is an individual assignment making up 20% of the overall unit assessment. The assessment criteria for this task are: 1) Apply machine learning pipeline to solve a real-world problem.
- Identify relevant data
- Process and clean data
- Transform data
- Apply and select machine learning techniques
- Analysis of the results.
- Identify the best technique for this problem.
Match between learning outcomes and criteria for the task:
STOCK PRICE PREDICTION
Description
In this assignment, we will apply machine learning techniques learned in the lectures and tutorials to predict the highest price of a stock in the next day.
- The date - "Date"
- The opening price of the stock - "Open"
- The low price of that day - "Low"
- The closed price of that day - "Close"
- The number of stocks traded during that day - "Volume"
- The high price of next day - "Next High"
Part A: Programming- 70%
In this task, you will train an AI model using the open price, the low price, and the volume of a day to predict the high price on the next day. In this task you need to design a RapidMiner process OR Python program to:
- Read the stock_data.csv file.
- Identify irrelevant information from the data and filter it out to construct the target data. Explain in the report how you do this.
- Identify the number of missing values in each attribute. Explain in the report.
- Fill the missing values by using the techniques you have learned. Explain the way you handled this issue in the report.
- Normalize the data so that all attributes are in the same range. Explain what method you used and what your chosen range was in the report. NOTE:
DON’T NORMALISE THE TARGET (LABEL) ATTRIBUTE.
- Decide your own strategy to train, evaluate a model from the data from stock_data.csv.
- Design your own strategy to select the best model.
- Apply the selected model to the data in predict_stock_data.csv. This data does not have the label and you have to generate the predicted value for the high price. Export the predicted high price into a CSV file using your student ID (for example 342435.csv).
When you complete the step 8, you will do stage-1 submission in week 13 (see below) to score the performance of your model.
Part B: Analysis- 30%
You will receive the performance score after week 13 and the ground truths. You will need to revise your design to:
- Explain why your model is good or bad. Write it in the report.
- What will you do to improve the prediction results if your model is scored as “NOT GOOD”. Write this in the report. Those whose models are scored as “GOOD” do not need to do this step.
Appendix 01: How to export an XML file for the model with RapidMiner
- Click File -> Export Process
- Choose save path -> Name file with ID -> Select Process File (*.xml)
Appendix 02: How to export a csv file for the predicted output using RapidMiner
- Use Select Attributes operator to filter output with only predicted high price attributes left
- Put Write CSV operator and connect with the filtered output
- Set csv file path
- Use symbol “,” for column separator
- Unselect quote nominal values
- Run process and output csv file
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