Rock_vs_Mine_Prediction.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"mYSLvRgB3Sel"},"source":["Importing the Dependencies"]},{"cell_type":"code","metadata":{"id":"cbE3ZjDb23el","executionInfo":{"status":"ok","timestamp":1695546688526,"user_tz":-330,"elapsed":2076,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}}},"source":["import numpy as np\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.metrics import accuracy_scorec"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"fCLGacZR4UZx"},"source":["Data Collection and Data Processing"]},{"cell_type":"code","metadata":{"id":"7ymxgj2i3RwO","executionInfo":{"status":"error","timestamp":1695546688526,"user_tz":-330,"elapsed":9,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}},"outputId":"8f53bd90-46c4-4865-a333-1885a25efdb8","colab":{"base_uri":"https://localhost:8080/","height":363}},"source":["#loading the dataset to a pandas Dataframe\n","sonar_data = pd.read_csv('/content/sonar data.csv', header=None)"],"execution_count":2,"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-2-65a17529d36a>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#loading the dataset to a pandas Dataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msonar_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/sonar data.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    948\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 950\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    951\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    952\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1733\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1736\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1737\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    854\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 856\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    857\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/sonar data.csv'"]}]},{"cell_type":"code","metadata":{"id":"I5iWxSnM42fl","executionInfo":{"status":"aborted","timestamp":1695546688527,"user_tz":-330,"elapsed":7,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}}},"source":["sonar_data.head()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"WN_FI_eN48V_","executionInfo":{"status":"aborted","timestamp":1695546688527,"user_tz":-330,"elapsed":7,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}}},"source":["# number of rows and columns\n","sonar_data.shape"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"q6A1r9J-5aOJ","executionInfo":{"status":"aborted","timestamp":1695546688527,"user_tz":-330,"elapsed":7,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}}},"source":["sonar_data.describe()  #describe --> statistical measures of the data"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"XFlxfDyk5o00","executionInfo":{"status":"aborted","timestamp":1695546688527,"user_tz":-330,"elapsed":7,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}}},"source":["sonar_data[60].value_counts()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"_6RDFTc26aBI"},"source":["M --> Mine\n","\n","R --> Rock"]},{"cell_type":"code","metadata":{"id":"Uis1XlFs6M09","executionInfo":{"status":"aborted","timestamp":1695546688527,"user_tz":-330,"elapsed":7,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}}},"source":["sonar_data.groupby(60).mean()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qRShuFc46jLd","executionInfo":{"status":"aborted","timestamp":1695546688528,"user_tz":-330,"elapsed":7,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}}},"source":["# separating data and Labels\n","X = sonar_data.drop(columns=60, axis=1)\n","Y = sonar_data[60]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"mkRRrxIe7D7l","executionInfo":{"status":"aborted","timestamp":1695546688528,"user_tz":-330,"elapsed":7,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}}},"source":["print(X)\n","print(Y)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"j912DrKe7L03"},"source":["Training and Test data"]},{"cell_type":"code","metadata":{"id":"bTnEFld87GIr","executionInfo":{"status":"aborted","timestamp":1695546688528,"user_tz":-330,"elapsed":7,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}}},"source":["X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.1, stratify=Y, random_state=1)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ww4D1Ps379_h","executionInfo":{"status":"aborted","timestamp":1695546688528,"user_tz":-330,"elapsed":7,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}}},"source":["print(X.shape, X_train.shape, X_test.shape)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"KBvcm4eR8enA","executionInfo":{"status":"aborted","timestamp":1695546688528,"user_tz":-330,"elapsed":7,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}}},"source":["print(X_train)\n","print(Y_train)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"rKLgrLOx8LQx"},"source":["Model Training --> Logistic Regression"]},{"cell_type":"code","metadata":{"id":"UoM3FhQS8FAw","executionInfo":{"status":"aborted","timestamp":1695546688528,"user_tz":-330,"elapsed":7,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}}},"source":["model = LogisticRegression()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"GGomegF-8TPv","executionInfo":{"status":"aborted","timestamp":1695546688529,"user_tz":-330,"elapsed":8,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}}},"source":["#training the Logistic Regression model with training data\n","model.fit(X_train, Y_train)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"585vgP7b8vBn"},"source":["Model Evaluation"]},{"cell_type":"code","metadata":{"id":"kCBykEtO8pLi","executionInfo":{"status":"aborted","timestamp":1695546688529,"user_tz":-330,"elapsed":8,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}}},"source":["#accuracy on training data\n","X_train_prediction = model.predict(X_train)\n","training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"50Wqy2Rc9nL1","executionInfo":{"status":"aborted","timestamp":1695546688529,"user_tz":-330,"elapsed":8,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}}},"source":["print('Accuracy on training data : ', training_data_accuracy)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"RCUZ6MuR9tOV","executionInfo":{"status":"aborted","timestamp":1695546688529,"user_tz":-330,"elapsed":7,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}}},"source":["#accuracy on test data\n","X_test_prediction = model.predict(X_test)\n","test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"04AsqCrz99vU","executionInfo":{"status":"aborted","timestamp":1695546688529,"user_tz":-330,"elapsed":7,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}}},"source":["print('Accuracy on test data : ', test_data_accuracy)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"RKrIzmr8-K9s"},"source":["Making a Predictive System"]},{"cell_type":"code","metadata":{"id":"NMp-UfOd-B7B","executionInfo":{"status":"aborted","timestamp":1695546688529,"user_tz":-330,"elapsed":7,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}}},"source":["input_data = (0.0307,0.0523,0.0653,0.0521,0.0611,0.0577,0.0665,0.0664,0.1460,0.2792,0.3877,0.4992,0.4981,0.4972,0.5607,0.7339,0.8230,0.9173,0.9975,0.9911,0.8240,0.6498,0.5980,0.4862,0.3150,0.1543,0.0989,0.0284,0.1008,0.2636,0.2694,0.2930,0.2925,0.3998,0.3660,0.3172,0.4609,0.4374,0.1820,0.3376,0.6202,0.4448,0.1863,0.1420,0.0589,0.0576,0.0672,0.0269,0.0245,0.0190,0.0063,0.0321,0.0189,0.0137,0.0277,0.0152,0.0052,0.0121,0.0124,0.0055)\n","\n","# changing the input_data to a numpy array\n","input_data_as_numpy_array = np.asarray(input_data)\n","\n","# reshape the np array as we are predicting for one instance\n","input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n","\n","prediction = model.predict(input_data_reshaped)\n","print(prediction)\n","\n","if (prediction[0]=='R'):\n","  print('The object is a Rock')\n","else:\n","  print('The object is a mine')\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"tcg9Er11_TSv","executionInfo":{"status":"aborted","timestamp":1695546688529,"user_tz":-330,"elapsed":7,"user":{"displayName":"Sagar Panwar","userId":"10687405784504381539"}}},"source":[],"execution_count":null,"outputs":[]}]}