This is a regression problem to predict california housing prices.

I have built a Model using Random Forest Regressor of California Housing Prices Dataset to predict the price of the Houses in California.

Machine Learning Housing Corp is a project that predicts median house values in Californian districts using various machine learning algorithms and dataset features such as average rooms, population density, and average income.

This project aims to develop a linear regression model to predict housing prices in California using the California Housing dataset. The project explores the impact of feature selection and scaling on the model's performance, with a focus on improving the accuracy of price predictions.

Build a model of housing prices to predict median house values in California using the provided dataset. Train the model to learn from the data to predict the median housing price in any district, given all the other metrics. Predict housing prices based on median_income and plot the regression chart for it.

An exploratory analysis of the California Housing dataset from Sci-kit Learn module.

Regression using CNN 1D for House price prediction on California Housing Dataset

Interactive California map with cities/counties comparison — housing costs, crime rates, population, temperature, sunshine, transit routes, and 3D terrain by county and city

# Bank Marketing Dataset ## Marketing Introduction: The process by which companies create value for customers and build strong customer relationships in order to capture value from customers in return. - Kotler and Armstrong (2010). Marketing campaigns are characterized by focusing on the customer needs and their overall satisfaction. Nevertheless, there are different variables that determine whether a marketing campaign will be successful or not. There are certain variables that we need to take into consideration when making a marketing campaign. ## The 4 Ps: 1) Segment of the Population: To which segment of the population is the marketing campaign going to address and why? This aspect of the marketing campaign is extremely important since it will tell to which part of the population should most likely receive the message of the marketing campaign. 2) Distribution channel to reach the customer's place: Implementing the most effective strategy in order to get the most out of this marketing campaign. What segment of the population should we address? Which instrument should we use to get our message out? (Ex: Telephones, Radio, TV, Social Media Etc.) 3) Price: What is the best price to offer to potential clients? (In the case of the bank's marketing campaign this is not necessary since the main interest for the bank is for potential clients to open depost accounts in order to make the operative activities of the bank to keep on running.) 4) Promotional Strategy: This is the way the strategy is going to be implemented and how are potential clients going to be address. This should be the last part of the marketing campaign analysis since there has to be an indepth analysis of previous campaigns (If possible) in order to learn from previous mistakes and to determine how to make the marketing campaign much more effective. ## What is a Term Deposit? A Term deposit is a deposit that a bank or a financial institurion offers with a fixed rate (often better than just opening deposit account) in which your money will be returned back at a specific maturity time. For more information with regards to Term Deposits please click on this link from Investopedia: https://www.investopedia.com/terms/t/termdeposit.asp ## Outline: 1. Import data from dataset and perform initial high-level analysis: look at the number of rows, look at the missing values, look at dataset columns and their values respective to the campaign outcome. 2. Clean the data: remove irrelevant columns, deal with missing and incorrect values, turn categorical columns into dummy variables. 3. Use machine learning techniques to predict the marketing campaign outcome and to find out factors, which affect the success of the campaign. ## Dataset Link https://archive.ics.uci.edu/ml/datasets/Bank+Marketing ## Dataset Information The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. There are four datasets: 1) bank-additional-full.csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014] 2) bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs. 3) bank-full.csv with all examples and 17 inputs, ordered by date (older version of this dataset with less inputs). 4) bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with less inputs). The smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM). The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y). ## Attribute Information Input variables: #### bank client data: 1-age (numeric) 2-job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 3-marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 4-education(categorical:'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 5-default: has credit in default? (categorical: 'no','yes','unknown') 6-housing: has housing loan? (categorical: 'no','yes','unknown') 7-loan: has personal loan? (categorical: 'no','yes','unknown') #### related with the last contact of the current campaign: 8-contact: contact communication type (categorical: 'cellular','telephone') 9-month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10-day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 11-duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model. #### other attributes: 12-campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 13-pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 14-previous: number of contacts performed before this campaign and for this client (numeric) 15-poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success') #### social and economic context attributes 16-emp.var.rate: employment variation rate - quarterly indicator (numeric) 17-cons.price.idx: consumer price index - monthly indicator (numeric) 18-cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19-euribor3m: euribor 3 month rate - daily indicator (numeric) 20-nr.employed: number of employees - quarterly indicator (numeric) Output variable (desired target): 21-y - has the client subscribed a term deposit? (binary: 'yes','no') ## License This dataset is public available for research. Citations - 1.Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014 2.Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

Predicting California housing prices using multi-model regression. Includes EDA, preprocessing, feature engineering, and model comparison (Linear Regression, Decision Tree, Random Forest, KNN) to identify the best predictor based on RMSE and R².

This project implements machine learning techniques to predict housing prices in California based on various features. The model utilizes both Stochastic Gradient Descent (SGD) and the Normal Equation methods to estimate property values, offering insights into the factors influencing real estate pricing in the region.

EDA and prediction of housing prices in California

California Housing Prices Prediction App

California Housing Training.csv

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simple EDA for house prices with Regression model

Optimized implementation of California Housing and Iris datasets using PyTorch.

In this model, I've used the california housing dataset from scikitlearn

A machine learning project to predict house prices using the California housing dataset.

This is the experimental assignment of my course "Machine Learning and Data Mining", which requires completing the training, testing and evaluation of the linear regression model for house price prediction based on the California Housing Prices dataset

House Price prediction machine learning project with CI/CD Pipeline

This is an educational workthrough project from the book "Hands-On ML with Scikit-Learn, Keras and TensorFlow" by Aurélien Géron. It is based on the well-known "California Housing Prices" dataset - through feature engineering I successfully improved the performance of the model used in the book.

Capstone Project - California Housing Price Prediction: Used linear, DT, ensemble regression techniques (Random Forests), feature scaling and feature engineering using Principal component Analysis (PCA); achieved minimal RMSE with ensemble technique. Supervised learning, Machine Learning, Python, Jupyter Notebook.

Welcome to Machine Learning Housing Corporation! The first task you are asked to perform is to build a model of housing prices in California using the California cen‐ sus data. This data has metrics such as the population, median income, median hous‐ ing price, and so on for each block group in California. Block groups are the smallest geographical unit for which the US Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people). We will just call them “dis‐ tricts” for short. Your model should learn from this data and be able to predict the median housing price in any district, given all the other metrics.

California house price prediction is done in this notebook

Python file for deploying the California Housing Price Prediction Streamlit Web App using AWS services

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This project focuses on predicting California housing prices using an XGBoost Regressor model trained on the California Housing dataset. The trained model is deployed via a Streamlit web application, allowing users to interactively input features and obtain price predictions.

python data science

A Streamlit app that predicts California median house prices using XGBoost and the California Housing dataset.