Common Tasks Report
9 / 9 / 2023
Task 1
3-D PRINTING
Date: 25th March 2023
- Downloading STL file: With the help of online platforms ( thingiverse, fusion 360 ) to download stl files, downloaded the desired object file.
- Slicing the file:. Using the all in one 3d printing platform, Creality slicing software 4.8.2 version we slice the stl file enabling us to move, rotate, scale and change the setting of the model.
- Uploading the g code: After slicing the time taken to print the object and amount of filament required is displayed and we make necessary changes.
- Copying code to printer: The g code is copied to the sd card of the 3D printer and given to print. The model ‘Baymax’ was printed in 2 hr 15 mins using creality slicer CR-10 Smart Pro.
Task 2
Working with API
Date: 16th March 2023
- Api: Application programming interface is a way for two or more computer programs to communicate with each other. It is a type of software interface. Here we are using weather api.
- Open API call: This product allows users to receive all essential weather data for a specific location by making only one API call. We used Open Weather API.
- Changing dynamics of the interface: Transferred the code inside a function to enable to provide weather for city given by user. Parameter ‘City’ was changed and my own key token was replaced in the code.
- Application on the website: The code was changed from static to dynamic and input of any city or place was given and the current weather was rightly displayed.
Task 3
Working with GitHub
Date: 14th March 2023
- We learnt how to fork a given repository ,then cloned it using the git clone command on git bash prompt.
- We had a create a new repository and under it a new branch and used the git checkout to list out to the branch before we made any changes.
- We opened the main.py file of the cloned file in our computer via vs code and checked for errors in the same.
- The errors in the code were corrected ,and by using git add . command we included the changes made in the next commit.
- Git commit --push origin command is used to take a snapshot of the changes made and then we push our changes to GitHub once we enter the username and email of our github account in the terminal.
Task 4
Working with command line on Ubuntu
Date: 20th March 2023
- Create a folder named test using the mkdir /tmp/ command.
- Cd into that folder using cd /tmp/
- Create a blank file without using any text editor.(mkdir
- List the files in that folder using “ls”
- Using for loop we created 2600 folders in this folder where each folder is named like le1 le2 etc.
- We used cat file1.txt>>file2.txt to concatenate two text files containing any random text and display them on the terminal.
Task 5
Kaggle Titanic ML Competition
Date: 20th March 2023
- The competition is uses machine learning to create a model that predicts which passengers survived the Titanic shipwreck.
- Join the Competition. Read about the challenge description, accept the Competition Rules and gain access to the competition dataset.
- Download the data, build models on it locally or on Kaggle Notebooks (Jupyter Notebooks) and generate a prediction file.
- Make a Submission. Upload your prediction as a submission on Kaggle and receive an accuracy score.
Task 6
Working with Pandas and Matplotlib
Date: 1st April 2023
- Pandas is a package commonly used to deal with data analysis. It simplifies the loading of data from external sources such as text files and databases.
- First, download the data by passing the download URL to pandas.read_csv(). Pandas has two core data structures used to store data: The Series and the DataFrame.
- The series is a one-dimensional array-like structure designed to hold a single array
- The DataFrame represents tabular data, a bit like a spreadsheet matplotlib is a Python package used for data plotting and visualisation.
Distributions and Histograms types:
Pie-
Scatter-
Hist-
Barh-
Task 7
Tinkercad
Date: 26th March 2023
- Arduino is an open-source electronics platform. It consists ATmega328 8-bit Microcontroller. It can be able to read inputs from different sensors & we can send instructions to the microcontroller in the Arduino.
- Setting up:
Connect the Echo pin of the sensor to the D2 pin of the Arduino. Connect the Trig pin of the sensor to the D3 pin of the Arduino. Navigate to Tools and select board and port. Verify and compile the code, then upload the code to the Arduino Uno R3 board. Monitor the output in the Serial monitor
- Steps to Interface LCD display:
Install driver library for Liquid Crystal Display. Import the header file “LiquidCrystal_I2C.h” in the code. Connect the SDA pin of an LCD display to the SDA pin of the Arduino Board. Connect VCC to 5V pin and GND to GND pin.
https://photos.google.com/photo/AF1QipMFHwOpflbTm6oNBaP1_1CrrX8UbHe73KQT08Pc
Task 8
LED Toggle
Date: 16th March 2023
- We are using the ESP32 DEVKIT DOIT board with 38 pins. Start by building the circuit. Connect two LEDs to the ESP32.
- Copy the code to your Arduino IDE. We need to tweak this code for changing login credential for your available WiFi network.
- Setting Your Network Credentials: We need to modify the following lines with your network credentials: SSID and password.
- After uploading the code, open the Serial Monitor at a baud rate of 115200.Press the ESP32 EN button (reset). The ESP32 connects to Wi-Fi, and outputs the ESP IP address on the Serial Monitor.
- Copy that IP address to access the ESP32 web server. Click the buttons to control the LEDs.
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Task 9
Soldering
Date: 25th March 2023
- Insert the LED leads into the circuit board holes and bend the leads outward at a 45-degree angle.
- Turn on the soldering iron and set the heat control to 400'C. Hold the tip of the iron to the copper pad and resistor lead for 3-4 seconds to heat the joint.
- Touch the solder to the joint, not directly to the iron tip.
4 .Remove the soldering iron and let the solder cool naturally, without blowing on it.
Once cool, snip the extra wire from the leads.
Domain Tasks
Task 1
Linear Regression - Prediction of California Housing Dataset
[Linear regression](https://github.com/Christina-26/Marvel-tasks-level-1/blob/main/linear_reg_california.ipynb \Linear regression")
Logistic Regression - Iris Flower Classification
Task 2
Matplotlib and Visualizing Data -Exploring the various basic characteristics to plots as given below, with python libraries. Exploring the various plot types.
Matplotlib and Visualizing Data
Task 3
Metrics and Performance Evaluation - Regression Metrics - used to evaluate performance of regression algorithms Classification Metrics - used to evaluate performance of classification algorithms
Metrics and Performance Evaluation
Task 4
Linear and Logistic Regression - are the two famous Machine Learning Algorithms which come under supervised learning technique.
Linear and Logistic Regression
Task 5
K- Nearest Neighbor Algorithm - K-NN algorithm assumes the similarity between the new case/data and available cases and put the new case into the category that is most similar to the available categories
Task 6
An elementary step towards understanding Neural Networks - My understanding of neural networks and how essential it is currently.
Task 7
Mathematics behind machine learning - Curve fitting using desmos, finds an optimal set of parameters for a defined function
Fourier transforms using Matlab - Model a fourier transform for a function of your choice on MATLAB.
Task 8
Data Visualization for Exploratory Data Analysis - Used Plotly for data visualization
Task 9
An introduction to Decision Trees - Decision Tree is a supervised learning algorithm that can be used for Regressive or Classifying tasks.
Task 10
Real world application of Machine Learning - Case study on Automatic vehicles
Level 2 Report
TASK 1
Develop a decision tree using the ID3 algorithm by leveraging instructional resources such as videos and articles. Understand the fundamental terminology, explore the intricacies of ID3 through tutorials, and implement the algorithm from scratch in Python. Utilize provided datasets and follow step-by-step guides to ensure a comprehensive grasp of the ID3 algorithm's functioning.
TASK 2
Implement the Naive Bayesian Classifier, a supervised learning algorithm based on Bayes' theorem, specifically tailored for text classification. Utilize resources such as videos and articles to understand the algorithm, and implement it for text classification using Python. Additionally, explore various use cases for the Naive Bayesian Classifier, considering its applicability beyond text classification.
TASK 3
Conduct Exploratory Data Analysis (EDA) on a dataset, leveraging various resources including videos and articles for guidance. Use examples of EDA to gain insights into the dataset, and refer to tutorials that explore different aspects of data analysis. In this specific task, focus on performing EDA on Airbnb data, formulating features from scraped data to aid in predicting listing prices.
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