DEV Community: UMAIR SHABBIR

15+ Free Websites and Tools That Seems Illegal to Know!

UMAIR SHABBIR — Tue, 02 Aug 2022 10:22:40 +0000

15+ Free Websites and Tools That Seems Illegal to Know!

Table of Content

Calculator
Clipchamp
Convertio
Jenni
Loom
Microcopy
OtterAI
Pexels
Photopea
QuillBot AI
RemoveBG
SaveFrom
ShortlyAI
Synthesia
Temp Mail
TinyWow

Calculator

Calculator.net's sole purpose is to provide free online calculators. They currently have 200 calculators available to assist you in a variety of fields. They are also working on expanding their offerings.

🔗 Calculator.net

Clipchamp

Create beautiful videos in no time with Clipchamp. Whether you need to save time on recording, save money on storage, or create an entire video from scratch, when it comes to anything video-related, Clipchamp is the best place to start.

🔗 Clipchamp

Convertio

Convertio - Easy online file conversion tool. More than 309 different formats of Documents, Images, Spreadsheets, E-Books, Archives, Presentations, Audio, and Video.

🔗 Convertio

Jenni

Jenni is the perfect writing assistant that saves you hours of brainstorming and writing. Write quality blogs at lightning speed.

🔗 Jenni

Loom

Record your screen and camera with one click. Share this content instantly with a link. Loom is the leading screen recording tool.

🔗 Loom

Microcopy

Search for Micro UX text: Taglines, Headlines, Alerts, Calls to Action, Error Messages, Email, Account Preferences, and much more.

🔗 Microcopy

OtterAI

Otter.ai uses artificial intelligence to enable users to transcribe real-time meeting notes that are shareable, searchable, accessible, and secure.

🔗 Otter.ai

Pexels

Free stock photos & videos you can use everywhere. Browse millions of high-quality royalty-free stock images & copyright-free pictures.

🔗 Pexels

Photopea

Photopea is a web-based photo and graphics editor. It is used for image editing, illustration creation, web design or conversion between different image formats. Photopea is an advertising supported software. It is compatible with all modern web browsers, including Opera, Edge, Chrome, and Firefox.

🔗 Photopea

QuillBot AI

The QuillBot paraphrasing tool helps millions of people rewrite and improve any Sentence, Paragraph, or Article using state-of-the-art AI.

🔗 QuillBot AI

RemoveBG

Remove image background 100% automatically and for free. Whether you want to make the background transparent (PNG) or add a white background to your photo, you can do all that and more with remove.bg.

🔗 remove.bg

SaveFrom

Video Downloader from SaveFrom.Net is an excellent service that helps you download online videos or music quickly and for free. You no longer need to install additional software or search for an online video download service. Here is the SaveFrom! It helps to download Online Video, TV shows, or Sports Games from many websites just by entering the video URL and clicking the Download button. SaveFrom.Net online video downloader Extension for Chrome is also available.

🔗 SaveFrom.Net

ShortlyAI

Gain typing skills with an AI typing partner. At the click of a button, ShortlyAI can continue writing for you, help you get your thoughts down on paper, and more.

🔗 ShortlyAI

Synthesia

Synthesia is a web platform for creating videos with AI Avatars. Thousands of companies use it to create professional videos at scale, 80% faster than before.

🔗 Synthesia

Temp Mail

Disposable email – is a free email service that allows you to receive emails to a temporary address that will self-destruct after a certain period of time. Temp-Mail - is the most advanced email service to help you avoid spam and stay safe.

🔗 Temp Mail

TinyWow

TinyWow provides free online Files Conversion, Images Conversion, PDF Tools, Videos Conversion and much more to help you in solving problems of all types. All processed and unprocessed files are deleted after 15 minutes.

🔗 TinyWow

Fin 👋

k-nearest neighbors algorithm (k-NN)

UMAIR SHABBIR — Sun, 26 Jun 2022 05:39:00 +0000

What is k-NN?

In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric classification method developed by Evelyn Fix and Joseph Hodges in 1951 and later expanded by Thomas Cover. Used for classification and regression. In both cases, the input includes training models close to the data set. Output depends on whether k-NN is used for classification or regression:

In the k-NN classification, the output of the class membership. An item is divided by a majority vote of its neighbors, the item is assigned the most common category among its closest neighbors and k. If k = 1, then the object is given to the class of that one nearest neighbor.
In k-NN regression, the output is the property value for the object. This value is the average value of k for nearby neighbors.

Dataset

Dataset used for the model is “Fish.csv”. Dataset consists of 159 rows and 7 columns.

Dataset Description

The attributes used in dataset are given bellow:

Species
Weight
Length1
Length2
Length3
Height
Width

Independent Attributes

Independent attributes in dataset are:

Length1
Length2
Length3
Height
Width

Dependent Attribute

Dependent attribute in dataset is:

Weight

Target Attribute

Target attribute in dataset is:

Weight

We will predict the weight of fish by using other attributes to train the model.

Dataset Head

from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
from math import sqrt
from sklearn import neighbors
from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('/content/Fish.csv')
df.head()

Here is the head of dataset used in the model.

Dataset Preprocessing

As we don't need Species attribute to predict the weight of fish. So, we will drop Species attribute.

df.drop(['Species'], axis=1, inplace=True)
df = pd.get_dummies(df)
df

The final dataset is given below:

Model Training and Testing

After preprocessing the dataset, we used preprocessed data for model training. For this purpose, we split up the data and select 30% of data for test purposes and 70% of data for model training. We test our model for k up to 20 to get minimum Root Mean Square Error (RMSE).

# Model Training
train, test = train_test_split(df, test_size=0.3)
x_train = train.drop('Weight', axis=1)
y_train = train['Weight']
x_test = test.drop('Weight', axis=1)
y_test = test['Weight']

%matplotlib inline

# Model Testing
rmse_val = []
for K in range(20):
    K = K+1
    model = neighbors.KNeighborsRegressor(n_neighbors=K)

    # Model Fitting
    model.fit(x_train, y_train)
    pred = model.predict(x_test)
    error = sqrt(mean_squared_error(y_test, pred))
    rmse_val.append(error)
    print('RMSE value for K = ', K, 'is:', error)

# Plotting RMSE values against value of K
curve = pd.DataFrame(rmse_val)
curve.plot()

The RMSE for different values of k are given below:

As it is clear from the above figure that RMSE is minimum for k = 3.

RMSE value for k = 3 is: 47.21824893415052

Prediction Results

So, we used k = 3 for the prediction of the weight of fishes. As we get minimum RMSE on 3 which is approximately 47.

# Model Fitting Minimum RMSE
model = neighbors.KNeighborsRegressor(n_neighbors=3)
model.fit(x_train, y_train)
pred = model.predict(x_test)
error = sqrt(mean_squared_error(y_test, pred))
rmse_val.append(error)  # store rmse values
print('RMSE value for K = ', 3, 'is:', error)

# Prediction Results
test['predicted weights'] = pred
test

The prediction results are given below:

References

Dataset Link: View Fish Dataset
Download Link: Download Fish Dataset
GitHub Repository: k-nearest neighbors algorithm (k-NN)

2021: Year in Review

UMAIR SHABBIR — Fri, 31 Dec 2021 12:12:00 +0000

Today's the last day of the year 2021. I joined DEV Community on Dec 29, 2021. So, I decided to publish 2021 Review as my 1st post.
Okay, Let's Start!

Highlights ✨

Oct-Dec

University 🎓 Re-Opening
GitHub
UET Career Fair 2021
Android Seekho Season 1
GDG Devfest 2021
Secret 🤫
DEV Community

University Opening

The most exciting thing of the year 2021 was University 🎓 reopening. University was reopened after approximately one and a half years. During this span, the classes were online which was so frustrating 😤 for me. Finally, University was reopened on Oct 13, 2021.

GitHub

The next thing I did in the year 2021, I joined GitHub on Oct 29, 2021 and update all my code work.

🔗 Here's my GitHub Account Link

UET Career Fair 2021

The second exciting thing of the year 2021 was UET Career Fair 2021. Which was held on Nov 18, 2021.

UET Career Fair 2021 Flyer

Android Seekho Season 1

Android Seekho Season 1 was the event organized for Pakistan Developer Community by Google. In this event, Android Basics in Kotlin were covered. I also participated in this event and completed all the units of the course.

Android Basics in Kotlin Completed

The next stage was the quiz. I cleared the quiz and qualified for the Android Seekho Season 1 Swag.

Quiz Cleared

Alhamdulillah! 😇
I received my Android Seekho Season 1 Swag

Android Seekho Season 1 Swag

GDG Devfest 2021

The most important and exciting thing of the year 2021 was GDG Devfest 2021. I attended the Devfest 2021 Lahore at The University of Central Punjab, Lahore on Dec 18, 2021.

Devfest 2021 Lahore

It was a great experience and I also learned a lot of new things about my field.

DEV Community

Finally, I joined DEV Community on Dec 29, 2021. It was the last thing of the year 2021 I did.

DEV Community Joined

Wrapping up

Overall, The year 2021 was a great year for me. I learned a lot of new things and gained skills. Hope the year 2022 comes with exciting things and opportunities for you and me.

Regards: UMAIR SHABBIR