{"id":20526647,"url":"https://github.com/abhirajp595/python","last_synced_at":"2026-05-08T15:05:10.080Z","repository":{"id":248802828,"uuid":"829826753","full_name":"Abhirajp595/Python","owner":"Abhirajp595","description":"Data Science Project using Python ","archived":false,"fork":false,"pushed_at":"2024-07-17T05:19:21.000Z","size":20464,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-16T11:27:34.710Z","etag":null,"topics":["data-analysis","data-science","data-visualization","eda","jyputer-notebook","numpy","pandas","statistics"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Abhirajp595.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-07-17T04:38:34.000Z","updated_at":"2024-07-17T06:34:12.000Z","dependencies_parsed_at":"2024-07-17T07:22:20.319Z","dependency_job_id":null,"html_url":"https://github.com/Abhirajp595/Python","commit_stats":null,"previous_names":["abhirajp595/python"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Abhirajp595%2FPython","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Abhirajp595%2FPython/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Abhirajp595%2FPython/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Abhirajp595%2FPython/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Abhirajp595","download_url":"https://codeload.github.com/Abhirajp595/Python/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242133430,"owners_count":20077095,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","data-science","data-visualization","eda","jyputer-notebook","numpy","pandas","statistics"],"created_at":"2024-11-15T23:15:12.566Z","updated_at":"2026-05-08T15:05:05.029Z","avatar_url":"https://github.com/Abhirajp595.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Python\n\n\nProject Statement:\nWhile searching for the dream house, the buyer looks at various factors, not just at the height of the basement ceiling or the proximity to an east-west railroad.\nUsing the dataset, find the factors that influence price negotiations while buying a house.\nThere are 79 explanatory variables describing every aspect of residential homes in Ames, Iowa.\n\n\nDataset Description:\n\nVariable: \tDescription\n\nSalePrice\t: The property's sale price is in dollars. This is the target variable that you're trying to predict.\n\nMSSubClass\t: The building class\n\nMSZoning\t: The general zoning classification\n\nLotFrontage\t : Linear feet of street connected to property\n\nLotArea\t: Lot size in square feet\n\nStreet\t: Type of road access\n\nAlley\tType : of alley access\n\nLotShape\t: General shape of property\n\nLandContour : Flatness of the property\n\nUtilities\t: Type of utilities available\n\nLotConfig\t: Lot configuration\n\nLandSlope\t: Slope of property\n\nNeighborhood\t: Physical locations within Ames city limits\n\nCondition1\t: Proximity to main road or railroad\n\nCondition2\t: Proximity to main road or railroad (if a second is present)\n\nBldgType\t: Type of dwelling\n\nHouseStyle\t: Style of dwelling\n\nOverallQual\t: Overall material and finish quality\n\nOverallCond\t: Overall condition rating\n\nYearBuilt\t: Original construction date\n\nYearRemodAdd\t: Remodel date\n\nRoofStyle\t: Type of roof\n\nRoofMatl\t: Roof material\n\nExterior1st\t: Exterior covering on house\n\nExterior2nd\t: Exterior covering on house (if more than one material)\n\nMasVnrType\t: Masonry veneer type\n\nMasVnrArea\t: Masonry veneer area in square feet\n\nExterQual\t: Exterior material quality\n\nExterCond\t: Present condition of the material on the exterior\n\nFoundation\t: Type of foundation\n\nBsmtQual\t: Height of the basement\n\nBsmtCond\t: General condition of the basement\n\nBsmtExposure\t: Walkout or garden level basement walls\n\nBsmtFinType1\t: Quality of the basement finished area\n\nBsmtFinSF1\t: Type 1 finished square feet\n\nBsmtFinType2\t: Quality of second finished area (if present)\n\nBsmtFinSF2\t: Type 2 finished square feet\n\nBsmtUnfSF : Unfinished square feet of basement area\n\nTotalBsmtSF\t: Total square feet of basement area\n\nHeating\t: Type of heating\n\nHeatingQC\t: Heating quality and condition\n\nCentralAir\t: Central air conditioning\n\nElectrical\t: Electrical system\n\n1stFlrSF\t: First Floor square feet\n\n2ndFlrSF\t: Second floor square feet\n\nLowQualFinSF\t: Low quality finished square feet (all floors)\n\nGrLivArea\t: Above grade (ground) living area square feet\n\nBsmtFullBath\t: Basement full bathrooms\n\nBsmtHalfBath\t: Basement half bathrooms\n\nFullBath\t: Full bathrooms above grade\n\nHalfBath\t: Half bathrooms above grade\n\nBedroom\t: Number of bedrooms above basement level\n\nKitchen\t: Number of kitchens\n\nKitchenQual :\tKitchen quality\n\nTotRmsAbvGrd\t: Total rooms above grade (does not include bathrooms)\n\nFunctional\t: Home functionality rating\n\nFireplaces\t: Number of fireplaces\n\nFireplaceQu\t: Fireplace quality\n\nGarageType\t: Garage location\n\nGarageYrBlt\t: Year garage was built\n\nGarageFinish\t: Interior finish of the garage\n\nGarageCars\t: Size of the garage in car capacity\n\nGarageArea\t: Size of the garage in square feet\n\nGarageQual\t: Garage quality\n\nGarageCond\t: Garage condition\n\nPavedDrive\t: Paved driveway\n\nWoodDeckSF\t: Wood deck area in square feet\n\nOpenPorchSF\t: Open porch area in square feet\n\nEnclosedPorch\t: Enclosed porch area in square feet\n\n3SsnPorch\t: Three season porch area in square feet\n\nScreenPorch\t: Screen porch area in square feet\n\nPoolArea\t: Pool area in square feet\n\nPoolQC\t: Pool quality\n\nFence\t: Fence quality\n\nMiscFeature\t: Miscellaneous feature not covered in other categories\n\nMiscVal\t: $Value of miscellaneous feature\n\nMoSold\t: Month Sold\n\nYrSold\t: Year Sold\n\nSaleType\t: Type of sale\n\nSaleCondition\t: Condition of sale\n\n\n\n\n\n\nPerform the following steps:\n1.\tUnderstand the dataset:\na.\tIdentify the shape of the dataset\nb.\tIdentify variables with null values\nc.\tIdentify variables with unique values\n\n2.\tGenerate a separate dataset for numerical and categorical variables\n3.\tEDA of numerical variables:\na.\tMissing value treatment\nb.\tIdentify the skewness and distribution\nc.\tIdentify significant variables using a correlation matrix \nd.\tPair plot for distribution and density\n4.\tEDA of categorical variables\na.\tMissing value treatment\nb.\tCount plot for bivariate analysis\nc.\tIdentify significant variables using p-values and Chi-Square values\n5.\tCombine all the significant categorical and numerical variables\n6.\tPlot box plot for the new dataset to find the variables with outliers\nNote: The last two points are performed to make the new dataset ready for training and prediction.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhirajp595%2Fpython","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabhirajp595%2Fpython","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhirajp595%2Fpython/lists"}