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    2
  • Language
    JavaScript
  • Created about 3 years ago
  • Updated about 3 years ago

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Repository Details

Repository of Aimtoget. Contains database of Old and Present Staff

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my-code-outlaws-project

This is a project done at the end of a 3-month Software Development training at Code Outlaws (online). Instructor: Benjamin Ayangbola @firstcodeoutlaw
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Zero-to-mastery-exercises

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9

Portfolio-SheCodeAfrica

Frontend first level Task on She Code Africa
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Code-Outlaw-Project

This is a repository for the projects done in CodeOutlaw.
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11

beable-devs

This is an update on the initial project given by @firstcodeoutlaw. A portfolio page with image sliders.
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Issys-Medcare

For this project, I was just playing around with tailwind using a template from youtube.
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13

House_Price_Prediction

This project used Python to predict house prices based on various features. I used sklearn library to perform regression on the training dataset and predicted the prices of the test set. I compared the predicted and actual values and generated a graph for the first 10 test samples.
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15

beable-bakes

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16

Beable-Coffe-Shop

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17

Text_based_Similarity_Matching_on_Medical_Dataset_using_Cosine_Similarity_measures

I implemented a text-based similarity matching system on a medical dataset using Python, read the data files in Pandas df and pre-processed the text. I created a TD Matrix with TF-IDF weighting and used CosSim to find the similarity between a query and documents. I searched the 10 top-ranked image ids for a query and showed the images in notebook.
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18

Sentiment_Analysis_Using_Different_Classifiers

This aimed to analyze the sentiment of a text dataset using different classifiers in Python. I used TF-IDF to represent the text and performed K-NN classification also trained the dataset with Random Forest, Logistic Regression, and Deep Neural Network using Keras. I evaluated performance of different classifiers on the test set
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19

Emotion_Dataset_Analysis

: This project involved classifying sentences by their emotions using text analysis techniques in Python. I pre-processed the text, generated vocabulary, and represented the text in different ways. I also built and compared three models: Logistic Regression, Neural Network, and K-means Clustering.
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