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GenAI – Internal Q & A Model
Gen AI – Q & A Model Table Of Contents: Define The Problem Statement. Steps To Complete This Use Case. (1) Define The Problem Statement (2) Step-by-Step Guide to Build HR Policy Assistant (RAG-based) (3) Collect & Prepare HR Documents. (1) Install Required Libraries pdfplumber python-docx unstructured pip install pdfplumber python-docx unstructured (2) Load and Extract Text from Documents import os import pdfplumber from docx import Document def load_text_from_pdf(file_path): text = “” with pdfplumber.open(file_path) as pdf: for page in pdf.pages: text += page.extract_text() + “n” return text def load_text_from_docx(file_path): doc = Document(file_path) return “n”.join([para.text for para in doc.paragraphs]) def load_text_from_txt(file_path):
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How LSTM Network Solves The Issue Of Vanishing Gradient Problem?
How Vanishing Gradient Problems Solved By The LSTM ? Table Of Contents: What Is Vanishing Gradient Problem? Why Does It Occurs In RNN? (1) What Is Vanishing Gradient Problem? The vanishing gradient problem is a challenge that occurs during the training of deep neural networks, particularly in networks with many layers. It arises when the gradients of the loss function, which are used to update the network’s weights, become extremely small as they are backpropagated through the layers of the network. This is the gradient calculation with only one layer. With only one layer you can see that three terms
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Image Classification With ANN!
Image Classification With ANN! Table Of Contents: What Is The Business Use Case? Importing Required Libraries. Loading Data. Shape Of Data. Data Types Of Dataset. Creating Validation Data And Scaling Data To Range (0-1). Looking At The First Two Images. Validation and Test Set Size. Let’s Look At A Sample Of The Images In The Dataset. Model Building. Compiling The Image Classification Model. Training & Evaluating Image Classification Model. Model Evaluation. Model Visualization. Visualizing Training And Validation Loss. Visualizing Training And Validation Accuracy. Making Prediction. Confusion Matrix. Looking At Some Random Prediction. Use The Model To Make Prediction. Here, the
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Life Expectancy Prediction – ANN!
Life Expectancy Prediction Table Of Contents: What Is The Business Use Case? Steps Involved In Heart Failure Prediction. Importing Library Loading Data Plotting Count Plot Examining The Correlation Matrix For All The Features. Examining Count Plot Of Age. Outlier Detection Plotting. KDE Plot. Data Preprocessing. Train Test Split. Model Building. Model Conclusion. (1) What Is The Business Use case ? This use case is all about the ‘Life Expectancy’ prediction of a person in a country using the ANN model. (2) Importing Required Libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn import preprocessing
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Heart Failure Prediction!
Heart Failure Prediction Table Of Contents: What Is The Business Use Case? Steps Involved In Heart Failure Prediction. Importing Library Loading Data Plotting Count Plot Examining The Correlation Matrix For All The Features. Examining Count Plot Of Age. Outlier Detection Plotting. KDE Plot. Data Preprocessing. Train Test Split. Model Building. Model Conclusion. (1) What Is The Business Use Case? Cardiovascular diseases are the most common cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Heart failure is a common event caused by Cardiovascular diseases. It is characterized by the
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Australian Rain Prediction.
Predicting Next Day Rain In Australia Table Of Contents: What Is The Business Use Case? Python Implementation. (1) What Is The Business Use Case ? Predicting next day rain using a dataset containing 10 years of daily weather observations from different location across Australia. (2) Python Implementation. (1) Importing Required Library import matplotlib.pyplot as plt import seaborn as sns import datetime from sklearn import preprocessing from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from keras.layers import Dense, BatchNormalization, Dropout, LSTM from keras.model import Sequential from keras.utils import to_categorical from keras.optimizer import Adam from tensorflow.keras import regularizers
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Customer Churn Prediction In The Banking Sector.
Customer Churn Prediction In Banking Sector Table Of Contents: What Is The Business Use Case? List Of Independent Variables. Importing Necessary Libraries for Artificial Neural Network. Importing Dataset. Generating Matrix of Features (X). Generating Dependent Variable Vector(Y). Encoding Categorical Variable Gender. Encoding Categorical Variable Country. Splitting Dataset into Training and Testing Dataset. Performing Feature Scaling. Initializing Artificial Neural Network. Creating Hidden Layers. Creating Output Layer. Compiling Artificial Neural Network. Fitting Artificial Neural Network. Predicting Result for Single Point Observation. Saving Created Neural Networks. (1) What Is Business Use Case ? Business Use Cases: Customer Churn Prediction In the Banking Sector.
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Data Science Use Cases List
Data Science Use Case List Table Of Contents: Customer Churn Prediction In Banking Sector.
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Data Science Use Cases
Data Science Use Cases List admin July 10, 2024 Data Science Use Cases Read More
