diff --git a/Project.ipynb b/Project.ipynb index 0f1b518..e8ce21c 100644 --- a/Project.ipynb +++ b/Project.ipynb @@ -60,8 +60,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-03-09T07:19:38.383986Z", - "start_time": "2024-03-09T07:19:38.006836Z" + "end_time": "2024-03-11T04:11:50.895427Z", + "start_time": "2024-03-11T04:11:50.344565Z" } }, "id": "883affb0ec11a93f", @@ -74,6 +74,7 @@ "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.decomposition import PCA\n", + "import matplotlib.pyplot as plt\n", "\n", "\n", "def clean_data(data):\n", @@ -83,6 +84,18 @@ " total_missing = data.isnull().sum().sum()\n", " print(f\"Total number of missing values: {total_missing}\")\n", "\n", + " # Statistical Analysis\n", + " print(\"Statistical Analysis:\")\n", + " print(data.describe())\n", + "\n", + " # Plot Boxplot to check for outliers for the first 15 columns\n", + " print(\"Boxplot of the first 15 columns:\")\n", + " fig, axs = plt.subplots(15,1,dpi=95, figsize=(7,17))\n", + " for i, col in enumerate(data.columns[:15]):\n", + " axs[i].boxplot(data[col], vert=False)\n", + " axs[i].set_ylabel(col)\n", + " plt.show()\n", + "\n", " # Data has no missing values\n", " data = data.dropna()\n", " print(\"Missing values dropped.\")\n", @@ -168,8 +181,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-03-09T07:19:38.891359Z", - "start_time": "2024-03-09T07:19:38.385473Z" + "end_time": "2024-03-11T04:11:52.029038Z", + "start_time": "2024-03-11T04:11:50.897462Z" } }, "id": "6da110e119fcb241", @@ -182,29 +195,119 @@ "name": "stdout", "output_type": "stream", "text": [ - "Loading data from pickle file...\n", - "Data loaded successfully.\n", + "Pickle file not found. Loading and cleaning data...\n", + "Original data shape: (42000, 1031)\n", + "Starting data cleaning process...\n", + "Total number of missing values: 0\n", + "Statistical Analysis:\n", + " NLOS RANGE FP_IDX FP_AMP1 FP_AMP2 \\\n", + "count 42000.000000 42000.000000 42000.000000 42000.000000 42000.000000 \n", + "mean 0.500000 3.831519 745.654167 8127.521905 11425.259524 \n", + "std 0.500006 2.355976 4.505024 5393.330697 6235.434769 \n", + "min 0.000000 0.000000 707.000000 7.000000 63.000000 \n", + "25% 0.000000 1.810000 744.000000 3573.750000 5322.750000 \n", + "50% 0.500000 3.480000 746.000000 7140.000000 12318.000000 \n", + "75% 1.000000 5.420000 748.000000 12273.000000 17310.000000 \n", + "max 1.000000 28.020000 848.000000 20572.000000 20624.000000 \n", + "\n", + " FP_AMP3 STDEV_NOISE CIR_PWR MAX_NOISE RXPACC \\\n", + "count 42000.000000 42000.000000 42000.000000 42000.000000 42000.00000 \n", + "mean 9738.106048 72.284571 9789.690214 1316.096524 616.27250 \n", + "std 5352.311549 29.318995 4912.556005 582.434989 306.78537 \n", + "min 187.000000 28.000000 0.000000 310.000000 128.00000 \n", + "25% 4723.000000 56.000000 6727.000000 961.000000 318.00000 \n", + "50% 10262.000000 68.000000 9950.500000 1134.000000 513.00000 \n", + "75% 14256.000000 80.000000 12443.000000 1488.000000 1024.00000 \n", + "max 20577.000000 324.000000 37208.000000 5169.000000 1056.00000 \n", + "\n", + " ... CIR1006 CIR1007 CIR1008 CIR1009 \\\n", + "count ... 42000.000000 42000.000000 42000.000000 42000.000000 \n", + "mean ... 257.455357 267.230833 249.123929 225.917143 \n", + "std ... 148.613220 155.703171 132.054825 113.041790 \n", + "min ... 1.000000 1.000000 0.000000 0.000000 \n", + "25% ... 161.000000 161.000000 161.000000 150.000000 \n", + "50% ... 233.000000 243.000000 233.000000 217.000000 \n", + "75% ... 313.000000 338.000000 304.000000 275.000000 \n", + "max ... 1593.000000 1497.000000 1172.000000 1169.000000 \n", + "\n", + " CIR1010 CIR1011 CIR1012 CIR1013 CIR1014 \\\n", + "count 42000.000000 42000.000000 42000.000000 42000.000000 42000.000000 \n", + "mean 239.445476 240.034286 254.388095 243.190643 253.173595 \n", + "std 131.519415 133.820366 177.274537 135.598470 145.780909 \n", + "min 0.000000 0.000000 0.000000 2.000000 2.000000 \n", + "25% 154.000000 154.000000 155.000000 154.000000 158.000000 \n", + "50% 225.000000 227.000000 229.000000 223.000000 230.000000 \n", + "75% 288.000000 292.000000 297.000000 295.000000 308.000000 \n", + "max 1315.000000 1595.000000 2153.000000 1428.000000 1709.000000 \n", + "\n", + " CIR1015 \n", + "count 42000.000000 \n", + "mean 90.203429 \n", + "std 145.839730 \n", + "min 0.000000 \n", + "25% 0.000000 \n", + "50% 0.000000 \n", + "75% 256.000000 \n", + "max 1280.000000 \n", + "\n", + "[8 rows x 1031 columns]\n", + "Boxplot of the first 15 columns:\n" + ] + }, + { + "data": { + "text/plain": "
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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Missing values dropped.\n", + "Duplicate rows dropped.\n", + "'NLOS' column converted to integer data type.\n", + "New feature 'FP_SUM' created.\n", + "New feature 'SNR' created.\n", + "Categorical features one-hot encoded.\n", + "'CIR' columns extracted.\n", + "PCA performed on 'CIR' columns.\n", + "DataFrame with principal components created.\n", + "Original 'CIR' columns dropped.\n", + "Indexes of both dataframes reset.\n", + "Dataframes concatenated.\n", + "Column 'CH' dropped due to having only one unique value.\n", + "Column 'BITRATE' dropped due to having only one unique value.\n", + "Column 'PRFR' dropped due to having only one unique value.\n", + "Numerical columns standardized.\n", + "Cleaned data shape: (42000, 49)\n", + "Data cleaning process completed.\n", + "Data loaded and cleaned successfully.\n", + "Saving cleaned data to pickle file...\n", + "Cleaned data saved to pickle file successfully.\n", "First few rows of the data:\n", " NLOS RANGE FP_IDX MAX_NOISE RXPACC FRAME_LEN PREAM_LEN \\\n", - "0 1 0.996831 0.742698 4.038101 1.329048 0.874210 -0.196211 \n", - "1 1 0.300720 -1.033118 -0.489497 1.329048 0.874210 -0.196211 \n", - "2 1 0.237051 -0.367187 -0.892980 1.329048 -1.143918 -0.196211 \n", - "3 1 -1.087256 0.520721 0.365545 -0.955965 0.874210 -0.196211 \n", - "4 0 -1.133947 -0.589164 1.212001 -1.043975 -1.143918 -0.196211 \n", + "0 0 0.029067 -0.145210 -0.599381 -0.017186 0.874210 -0.196211 \n", + "1 0 -1.346176 0.742698 -0.314368 -0.551769 -1.143918 -0.196211 \n", + "2 1 1.709919 0.076767 -0.724719 0.347894 -1.143918 -0.196211 \n", + "3 1 -0.149205 0.964675 -0.324669 1.329048 0.874210 -0.196211 \n", + "4 0 -1.121213 0.076767 0.734689 -1.109168 -1.143918 -0.196211 \n", "\n", " CIR1015 FP_SUM SNR ... PC30 PC31 PC32 PC33 \\\n", - "0 4.647599 -0.004151 -1.343887 ... 0.611150 -0.759119 -0.388000 0.512109 \n", - "1 -0.618518 -1.557666 -0.659387 ... -0.070661 0.472775 0.102545 -0.201952 \n", - "2 -0.618518 -1.383667 -1.135516 ... -0.820217 -0.045836 -0.774702 -0.657465 \n", - "3 -0.618518 0.997584 0.578555 ... 0.207415 -0.372339 0.338298 0.203412 \n", - "4 -0.618518 1.770317 0.011391 ... -0.103616 -0.279537 -2.082591 0.689179 \n", + "0 -0.618518 0.754428 0.394452 ... 0.389832 -0.142670 1.412862 -1.097675 \n", + "1 -0.618518 -0.471346 1.703977 ... 0.496051 1.696266 0.090825 -0.176461 \n", + "2 -0.618518 -1.087855 1.098450 ... -0.610514 -1.022127 0.708134 1.163720 \n", + "3 1.136854 -0.434856 -0.431851 ... -0.501000 0.060824 0.137925 0.605377 \n", + "4 1.136854 1.254826 0.183762 ... 0.081076 0.117554 -0.530323 0.486816 \n", "\n", " PC34 PC35 PC36 PC37 PC38 PC39 \n", - "0 -0.722884 0.071196 0.949064 -0.135764 -0.216211 0.137419 \n", - "1 0.654153 1.152224 0.731853 -0.817268 -0.222097 -0.234517 \n", - "2 -0.857873 0.198621 0.978201 0.570538 -0.077174 -0.668429 \n", - "3 0.356535 -0.319102 0.070815 0.297599 0.020617 -0.301117 \n", - "4 -0.083663 0.167116 -0.545838 0.533610 -0.085079 0.258466 \n", + "0 -0.162535 -0.338152 0.048162 -0.545754 0.293655 0.348369 \n", + "1 3.124448 0.457156 -0.267002 -0.744417 0.304645 -0.912732 \n", + "2 -1.461944 1.501136 2.499866 1.157795 -0.517617 2.339966 \n", + "3 0.407462 -1.234992 2.244721 2.658034 -1.237848 -0.027678 \n", + "4 0.712877 -0.177848 -0.277695 -0.084126 -0.077211 -0.285837 \n", "\n", "[5 rows x 49 columns]\n", "Column headers:\n", @@ -252,8 +355,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-03-09T07:19:38.918418Z", - "start_time": "2024-03-09T07:19:38.892609Z" + "end_time": "2024-03-11T04:12:07.302222Z", + "start_time": "2024-03-11T04:11:52.030075Z" } }, "id": "e01fe23e950f89a",