Sonar_Detection.ipynb

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    "## Classification: Detect Pipe or Rock with Sonar Data\n",
    "\n",
    "Classification of Rock or Mine with [sonar data](https://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+(Sonar,+Mines+vs.+Rocks)). Sonar (sound navigation and ranging) uses sound waves to detect objects, similar to how a bat uses echo-location to navigate, and detect objects. It is the same principle with seismic data for non-invasive underground exploration of geologic formations to locate oil or gas reserves.\n",
    "\n",
    "```python\n",
    "url = 'http://apmonitor.com/pds/uploads/Main/sonar_detection.txt'\n",
    "```\n",
    "\n",
    "The data set of sonar is taken from different angles and locations to detect the rock or pipe. The data was collected in a laboratory under controlled conditions as a case study for detecting underground pipe. There are 111 labeled sets for the metal cyclinder (pipe) and 97 sonar patterns from rocks with similar conditions. Each sample is a set of 60 numbers between 0 and 1 that represents the integrated energy within a distinct frequency band and for a given time period.\t\n",
    "\n",
    "[Pipe / Rock Sonar Case Study](https://apmonitor.com/pds/index.php/Main/SonarDetection) on [Machine Learning for Engineers](https://apmonitor.com/pds/index.php/Main/SonarDetection)\n",
    "\n",
    "Although this case study is specifc to detecting differences between metal pipe and rock, it is similar to detection of other underground features such as tunnels, mines, aquifers, and fluid-filled pipelines.\n",
    "\n",
    "<img align=left width=400px src='https://apmonitor.com/pds/uploads/Main/sonar_detection.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Packages\n",
    "\n",
    "Import *pandas*, *matplotlib*, and other packages needed for this exercise. These are amoung the [several common packages](https://apmonitor.com/pds/index.php/Main/InstallPythonPackages) used in Data Science to import, visualize, cleanse data, perform classification, and analyze the results."
   ]
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  {
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  },
  {
   "cell_type": "markdown",
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   "source": [
    "### Read Data\n",
    "\n",
    "Read data as a Pandas dataframe with `data=pd.read_csv(url)`. Show 10 random rows with `data.sample(10)`. There is additional information on [data importing](https://apmonitor.com/pds/index.php/Main/GatherData)."
   ]
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  {
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  {
   "cell_type": "markdown",
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   "source": [
    "### Data Visualization\n",
    "\n",
    "Create plots to visualize the data. Below are a few sample plots as shown in [Data Visualization](https://apmonitor.com/pds/index.php/Main/VisualizeData).\n",
    "\n",
    "Create a box plot."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "data.plot(kind='box', subplots=True, layout=(6,10),\\\n",
    "             sharex=False, legend=False, fontsize=1, \\\n",
    "             figsize=(12,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the grouping by `Class`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.groupby('Class').size()"
   ]
  },
  {
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    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data['Class'].value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate a Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# correlation matrix\n",
    "from matplotlib import cm\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(data.corr(), vmin=-1, vmax=1, cmap=cm.Spectral_r, interpolation='none')\n",
    "fig.colorbar(cax)\n",
    "fig.set_size_inches(10,10)\n",
    "plt.savefig('sonar_correlation.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate a Density Plot"
   ]
  },
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   "cell_type": "code",
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   "outputs": [],
   "source": [
    "data.plot(kind='density', subplots=True, layout=(6,10),\\\n",
    "             sharex=False, legend=False, fontsize=1, \\\n",
    "             figsize=(12,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate a Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.hist(sharex=False, sharey=False, layout=(6,10),\\\n",
    "        xlabelsize=1, ylabelsize=1, figsize=(12,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additional data visualization to identify outliers, feature correlation, and any data quality issues."
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What insights do you gain from the data visualization and exploration? In particular, comment on the presence of uniform data distributions, outliers, missing data, and other data quality issues."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scale Data\n",
    "\n",
    "Scale data with a [Standard Scalar](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) so that all features (sonar returns) are scaled. "
   ]
  },
  {
   "cell_type": "code",
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  },
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   "cell_type": "markdown",
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   "source": [
    "### Best Features\n",
    "\n",
    "What are the factors that are most correlated or influential for predicting pipe versus rock. Use the `SelectKBest` package to produce a ranked list."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "### Train / Test Split\n",
    "\n",
    "Split the data into **Test** and **Train** sets. Randomly select values that split the data into a train (80%) and test (20%) set by using the sklearn `train_test_split` with `shuffle=True`. "
   ]
  },
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    "### Import Classifier Packages\n",
    "\n",
    "Classification: Use 8 classification methods. Possible regression methods are:\n",
    "\n",
    "- AdaBoost\n",
    "- Logistic Regression\n",
    "- Naïve Bayes\n",
    "- Stochastic Gradient Descent\n",
    "- K-Nearest Neighbors\n",
    "- Decision Tree\n",
    "- Random Forest\n",
    "- Support Vector Classifier\n",
    "- Deep Learning Neural Network\n",
    "\n",
    "The [Scikit-learn documentation](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html) has additional information on classifiers."
   ]
  },
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Classifiers"
   ]
  },
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   "cell_type": "code",
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   "cell_type": "markdown",
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   "source": [
    "### Show Confusion Matrix Result\n",
    "\n",
    "A confusion matrix shows true positive, false positive, true negative, and false negative groups from the test set.\n",
    "\n",
    "```python\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "```\n",
    "\n",
    "Generate a confusion matrix for each classifier."
   ]
  },
  {
   "cell_type": "code",
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   "cell_type": "markdown",
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   "source": [
    "### Interpretation of Results\n",
    "\n",
    "Write an executive summary (max 2 page report) on the result of the classifiers from the sonar data set. Report the confusion matrix in the test set. What are recommendations to detect pipe versus rock?"
   ]
  }
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