{"id":29216968,"url":"https://github.com/priyankapriyadarshini50/autos_data_analysis","last_synced_at":"2026-04-09T01:32:05.714Z","repository":{"id":290347496,"uuid":"967205422","full_name":"priyankapriyadarshini50/autos_data_analysis","owner":"priyankapriyadarshini50","description":"Data Analysis of auto dataset","archived":false,"fork":false,"pushed_at":"2025-07-01T01:47:25.000Z","size":182,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-09-08T16:50:31.639Z","etag":null,"topics":["matplotlib","numpy","pandas","psycopg2","scikit-learn","scipy","seaborn"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/priyankapriyadarshini50.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-04-16T05:10:46.000Z","updated_at":"2025-07-01T01:47:28.000Z","dependencies_parsed_at":null,"dependency_job_id":"5289ef1a-1b04-4f31-8866-c3c8c1cf0cbd","html_url":"https://github.com/priyankapriyadarshini50/autos_data_analysis","commit_stats":null,"previous_names":["priyankapriyadarshini50/autos_data_analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/priyankapriyadarshini50/autos_data_analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/priyankapriyadarshini50%2Fautos_data_analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/priyankapriyadarshini50%2Fautos_data_analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/priyankapriyadarshini50%2Fautos_data_analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/priyankapriyadarshini50%2Fautos_data_analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/priyankapriyadarshini50","download_url":"https://codeload.github.com/priyankapriyadarshini50/autos_data_analysis/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/priyankapriyadarshini50%2Fautos_data_analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31581864,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-08T14:31:17.711Z","status":"ssl_error","status_checked_at":"2026-04-08T14:31:17.202Z","response_time":54,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["matplotlib","numpy","pandas","psycopg2","scikit-learn","scipy","seaborn"],"created_at":"2025-07-03T02:00:33.810Z","updated_at":"2026-04-09T01:32:05.687Z","avatar_url":"https://github.com/priyankapriyadarshini50.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"### Auto Data Analysis Project\nThis project provides the correlation between the different parameters of auto dataset. It reads the data from a csv file\nand prepare data for analysis by cleaning and replacing missing values, standardize and normalize the data, binning the data\nfor visualization, converting categorial to columns and finding the correlation between the variable such as horsepower and price,\nengine-size and price etc.\n\n### raw data:\nCAR_DATASET_URL=\"https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data\"  \n\n   |symboling| normalized_losses|make      |fuel_type| aspiration| num_of_doors|  ... |compression-ratio| horsepower| peak_rpm | city_mpg|highway_mpg|price\n   |---------|------------------|----------|---------|-----------|-------------|------|-----------------|-----------|----------|---------|----------|---------|\n0  |       3 |               ?  |alfa-romero|     gas|       std |        two  |...   |           9.0   |    111    | 5000     |  21     |     27   |13495\n1  |       3 |               ?  |alfa-romero|     gas|       std |        two  |...   |           9.0   |    111    | 5000     |  21     |     27   |16500\n2  |       1 |               ?  |alfa-romero|     gas|       std |        two  |...   |           9.0   |    154    | 5000     |  19     |     26   |16500\n3  |       2 |             164  |      audi |     gas|       std |       four  |...   |          10.0   |    102    | 5500     |  24     |     30   |13950\n4  |       2 |             164  |      audi |     gas|       std |       four  |...   |           8.0   |    115    | 5500     |  18     |     22   |17450\n\n*getting total number of null values based on columns:*  \nnumber of NaN values for the column normalized_losses : 41  \nnumber of NaN values for the column price : 4  \n\nUpdated the datatype to correct format:  \nData types assigned to pandas df=  \nsymboling               int64  \nnormalized_losses       int64  \nmake                   object  \nfuel_type              object  \naspiration           category  \nnum_of_doors           object  \nbody-style             object  \ndrive_wheels           object  \nengine_location        object  \nwheel_base            float64  \nlength                float64  \nwidth                 float64  \nheight                float64  \ncurb_weight             int64  \nengine_type            object  \nnum_of_cylinders       object  \nengine_size             int64  \nfuel_system            object  \nbore                  float64  \nstroke                float64  \ncompression-ratio     float64  \nhorsepower            float64  \npeak_rpm              float64  \ncity_mpg                int64  \nhighway_mpg             int64  \nprice                 float64  \ndtype: object  \n\n*The horsepower column data has been binned that provides better data visualization.*  \n\u003cp align=\"left\"\u003e\n  \u003cimg src=\"./static/images/data_binning_auto.png\" alt=\"Preview\" width=\"400\"\u003e\n\u003c/p\u003e  \nThis histogram shows that most of the cars in the data set has Low horsepower.\n \n\nAfter data cleaning, the correlation data for some columns:\n|                  |   bore|   stroke|compression-ratio|horsepower| engine_size|   price|\n|------------------|-------|---------|-----------------|----------|------------|--------|\n|bore              |1.000000|-0.055390|        0.001263|  0.566936|    0.572609|0.543155|\n|stroke            |-0.055390|1.000000|        0.187871|  0.098267|    0.205928|0.082269|\n|compression-ratio |0.001263 |0.187871|        1.000000| -0.214514|    0.028889|0.071107|\n|horsepower        |0.566936 |0.098267|       -0.214514|  1.000000|    0.822676|0.809575|\n|engine_size       |0.572609 |0.205928|        0.028889|  0.822676|    1.000000|0.872335|\n|price             |0.543155 |0.082269|        0.071107|  0.809575|    0.872335|1.000000|\n\nFrom the above table, we can find out there is a positive correlation between engine size and price,\nhorsepower and size.  \n\ndb_connection is a script which gets data from db table using sql query.\n### Data Visualization  \n\u003cp align=\"left\"\u003e\n  \u003cimg src=\"./static/images/Figure_1_scattered_plot.png\" alt=\"Preview\" width=\"400\"\u003e\n\u003c/p\u003e  \nThe above scattered regression plot shows that \"Engine size\" as potential predictor variable of \"price\"\n\n\u003cp align=\"left\"\u003e\n  \u003cimg src=\"./static/images/Figure_3_box_plot.png\" alt=\"Preview\" width=\"400\"\u003e\n\u003c/p\u003e  \nThe above box plot shows the relationship between categorical variables \"body-style\" and \"price\"  \n\n\u003cp align=\"left\"\u003e\n  \u003cimg src=\"./static/images/Figure_2_heatmap.png\" alt=\"Preview\" width=\"400\"\u003e\n\u003c/p\u003e  \nThe above box plot shows the relationship between categorical variables \"body-style\", \"wheel-base\" and \"price\"\nWe can see cars with convertible body style and rwd wheel-base has maximum price.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpriyankapriyadarshini50%2Fautos_data_analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpriyankapriyadarshini50%2Fautos_data_analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpriyankapriyadarshini50%2Fautos_data_analysis/lists"}