House Price Prediction Through Machine Learning ML CD

Slide 1: House Price Prediction Through Machine Learning. State your company name and begin.
Slide 2: This is an Agenda slide. State your agendas here.
Slide 3: This slide shows Table of Content for the presentation.
Slide 4: This slide shows title for topics that are to be covered next in the template.
Slide 5: This slide showcases current problems faced by organization due to usage of traditional house prediction model such as market fluctuations, limited accuracy etc.
Slide 6: This slide presents difference in actual and predicted property price through traditional methods. It highlights major variance in different property prices
Slide 7: This slide shows title for topics that are to be covered next in the template.
Slide 8: This slides showcases machine learning benefits that can help to solve various problems faced by company due to traditional prediction methods.
Slide 9: This slide illustrates machine learning process for house price prediction. It includes data collection, feature engineering, model selection, deployment, evaluation etc.
Slide 10: This slide shows title for topics that are to be covered next in the template.
Slide 11: This slide illustrates machine learning concepts highlighting its ability to learn from data to predict outcomes. It showcases key usages such as data mining, NLP, fraud detection etc.
Slide 12: This slide showcases various types of machine learning algorithms that can help to analyze and generate insights from the data. Its key elements are supervised learning, clustering etc.
Slide 13: The slide compares traditional modeling with machine learning approaches. It outlines their respective methods and shows benefits of traditional modeling over machine learning.
Slide 14: This slide shows title for topics that are to be covered next in the template.
Slide 15: The slide provides the introduction of house price prediction data collection in machine learning. It enlists major attributes including property location, number of bedrooms etc.
Slide 16: The slide highlights Kaggle house prices, California housing prices, Boston housing dataset, Ames housing dataset and Melbourne housing market datasets.
Slide 17: The slide illustrates house dataset with area type, availability, location, size and historical price. It is utilized to develop house price prediction model.
Slide 18: This slide highlights list of parameters in checklist for effective data collection in house price prediction. It covers ensuring clear objectives, reliable sources etc.
Slide 19: This slide shows title for topics that are to be covered next in the template.
Slide 20: This slide showcases data preprocessing and can transform raw information. It also highlights benefits and steps involved in preprocessing.
Slide 21: This slide presents preprocessing of housing dataset features such as living area size, age of property, overall property condition etc.
Slide 22: This slide shows title for topics that are to be covered next in the template.
Slide 23: This slide showcases process that can help to conduct exploratory analysis on housing dataset. Its key steps are data collection, cleaning, identify correlated variables etc.
Slide 24: The slide presents correlation analysis to assess variables impact on house prices. It identifies features strongly correlated with sales price, assisting in predictive modeling etc.
Slide 25: This slide highlights statistics for numerical and categorical features in exploratory data analysis. It includes mean, median, min, max and standard deviation etc.
Slide 26: This slide shows title for topics that are to be covered next in the template.
Slide 27: This slide showcases introduction to feature extraction that can help in prediction process of machine learning. It also highlights key benefits of feature extraction.
Slide 28: This slide showcases techniques such as autoencoders, principal component analysis and bag of words that can help in feature extraction.
Slide 29: The slide covers key features needed for house price prediction. It includes numerical, ordinal, categorical, and binary categories etc.
Slide 30: The slide presents important features to consider for house price prediction, including property details like bedrooms, bathrooms, square footage, amenities etc.
Slide 31: This slide shows title for topics that are to be covered next in the template.
Slide 32: This slide showcases introduction to decision tree model which is used for various regression and classification tasks in machine learning.
Slide 33: The slide demonstrates decision tree model for house price prediction based on features like OverallQual, TotalBsmtSF, and GrLivArea.
Slide 34: This slide shows title for topics that are to be covered next in the template.
Slide 35: This slide showcases introduction to linear regression model that can help organization in predictive analytics. It also highlights various benefits of linear regression.
Slide 36: This slide illustrates process of using linear regression to predict house prices by defining dependent and independent variables leading to the outcome of interpreting coefficients.
Slide 37: This slide shows title for topics that are to be covered next in the template.
Slide 38: This slide showcases introduction to neural network which is used for prediction in machine learning. It includes three layers called input, output and hidden layer.
Slide 39: This slide presents neural network that can help in house price prediction. It include three layers which are input, output and hidden.
Slide 40: This slide shows title for topics that are to be covered next in the template.
Slide 41: This slide showcases introduction to random forest model that combines multiple decision trees to make accurate predictions. It also highlights key features of model.
Slide 42: The slide demonstrates random forest usage for house price prediction. It leverages multiple decision trees to compute average final prediction for final and accurate value prediction.
Slide 43: This slide shows title for topics that are to be covered next in the template.
Slide 44: This slide showcases machine learning model training overview that can help to make accurate house price prediction model.
Slide 45: This slide displays various components such as data, algorithm, parameters, loss function that can help in machine learning model training.
Slide 46: This slide showcases process that can help in machine learning model training. Its key steps are collecting and preparing data, selecting right algorithm and splitting data.
Slide 47: This slides presents various tools that can help organization to train machine learning model for house price prediction. Its key elements are framework type, language support, backend etc.
Slide 48: This slide shows title for topics that are to be covered next in the template.
Slide 49: This slide showcases various methods that can help to deploy machine learning model. Methods highlighted are batch deployment, real-time and streaming deployment.
Slide 50: This slide displays strategies that can help organization to deploy machine learning model. Various strategies are shadow deployment, A/B testing , blue/green etc.
Slide 51: This slide shows title for topics that are to be covered next in the template.
Slide 52: This slide showcases various metrics such as accuracy, precision, recall, F1 score that can help to assess performance of machine learning models.
Slide 53: This slide shows title for topics that are to be covered next in the template.
Slide 54: This slide showcases impact of leveraging machine learning model for house price prediction. It highlights benefits such as risk management, cost savings etc.
Slide 55: This slide shows all the icons included in the presentation.
Slide 56: This slide is titled as Additional Slides for moving forward.
Slide 57: This is Our Team slide with names and designation.
Slide 58: This slide shows SWOT analysis describing- Strength, Weakness, Opportunity, and Threat.
Slide 59: This is About Us slide to show company specifications etc.
Slide 60: This is a Timeline slide. Show data related to time intervals here.
Slide 61: This slide describes Line chart with two products comparison.
Slide 62: This slide presents Roadmap with additional textboxes. It can be used to present different series of events.
Slide 63: This slide depicts Venn diagram with text boxes.
Slide 64: This slide shows Pie Chart with data in percentage.
Slide 65: This is a Thank You slide with address, contact numbers and email address.