In this blog we will provide some important machine learning and data science problem set that can help you to improve your skills in machine learning. We are providing any type of machine learning and data science project help, assignment help and homework help services with an affordable prices.

**Task 1**

**Pandas (for basic understanding) Practice**

**Questions**

1. Please go through the 16 questions in the attached ipynb

2. Write well documented code to solve those questions and print proper output for all of them.

**Note-1: **If you have used code from somewhere, provide those references or citations. Else it will be considered plagiarism.

**Note-2: **Upload both python notebook and pdf version of that notebook.

**Note-3:** For converting Jupyter notebook to PDF please have a look at this

link:https://stackoverflow.com/questions/15998491/how-to-convert-ipython-notebooks-to-pdf-and-html.

**Task 2**

**Python Practice Questions**

Click __here__ to download .ipynb notebook

1. There are a total of 10 questions in the attached ipynb file. (for the 10th question there is a type it should be 0.42)

2. The question is explained along with an example as well in the jupyter notebook itself.

3. Write well-documented code to solve those questions and print proper output for all of them.

4. Note that you are not supposed to use any libraries like numpy or scipy or similar libraries for this. You have to do this assignment completely from scratch in python and its functionalities.

This is a mandatory python assignment.

https://medium.com/@appliedaicourse_56208/faqs-of-python-mandatory-

assignment-7ada380ec770

**Task 3**

**Exploratory Data Analysis on Haberman Dataset**

Necessary files can be downloaded from the following links:

1. The data and reference notebook is attached here, try to document every plot and analysis that you do.

2. Experiment with different functionalities of jupyter notebook and get habituated with its features.

3. Try out as many plotting techniques as you can, but write down your observations for each of them.

4. Please be sure to have proper axes names, title and legend to all the charts that you plot.

5. Have a proper conclusions section where in you summarise your overall observation.

6. If you want to explore more about Haberman's Survival Data Set, you can try out this __link ____https://www.kaggle.com/gilsousa/habermans-survival-data-set/version/1__

**Task 4**

**Implementing TFIDF vectorizer**

Please check the video before working on the assignment

1. Please check the google drive__ link__ to download .ipynb files

2. We have given two ipython notebooks (1.Assignment_3_Reference.ipynb) in which we have implemented countvectorizer, (2.Assignment_3_Instructions.ipynb) you need to implement tfidf vectorizer, we have given the complete instructions in this notebook

**Task 5**

**Implement RandomSearchCV with k fold cross validation on KNN**

Please check the video before working on the assignment

1. Please check the google drive __link__ to download .ipynb files

2. we have give two ipython notebooks (1.Assignment_4_Reference.ipynb) in which we have implemented GridsearchCV (2. Assignment_4_Instructions.ipynb) you need to implement

RandomsearchCV, we have given the complete instructions in this notebook

**Task 6**

Compute Performance metrics without Sklearn

Please check the video before working on the assignment

Please check the google drive __link__ to download .ipynb files

Assignment instructions are mentioned in

6_Performance_metrics_Instructions.ipynb

**Task 7**

Apply Naive Bayes on Donors Choose dataset

Please check the video before working on the assignment

You can download the relevant files from the drive from here...

WRITE YOUR CODE IN THE SAME NOTEBOOKS

**Task 8**

**Behavior of Linear Models**

1. In this assignment, you will be solving 6 subproblems

2. Each subproblem will be having subtasks

3. Check the google drive folder to find out the instructions of each sub-problem:

4. You can submit the assignment in 6 different notebooks or in a single note which will have all solutions to all the subproblems.

**Task 9**

**Apply Decision Trees on Donors Choose dataset**

Please check the video before working on the assignment

Necessary files can be downloaded from here

**Task 10**

1. Please check the google drive__ link__ to download .ipynb files

2. We have given two ipython notebooks (1.Central_Limit_theorem.ipynb) in which we have explained concepts of Confidence interval,(2. Bootstrap_Random_Forest_instructions.ipynb) you need to complete the

3 tasks that are given in this notebook

To get any help in above task or any other task you can contact us at:

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