WaterQuality (1).ipynb

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      "source": [
        "# pip install rpy2"
      ]
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      "source": [
        "# ipython   rkernel"
      ]
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      "source": [
        "df <- read.csv('water_potability.csv')"
      ]
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      "outputs": [],
      "source": [
        "head(df)"
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    {
      "cell_type": "code",
      "execution_count": null,
      "id": "975685ac-782a-40ff-bfb1-26de9752799b",
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      "source": [
        "print(summary(df))"
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      "source": [
        "print(str(df))"
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      "source": [
        "\n",
        "dim(df)"
      ]
    },
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      "outputs": [],
      "source": [
        "nrow(df)\n"
      ]
    },
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      "id": "cd87d220-d280-4c77-9711-84188a0360bc",
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      },
      "outputs": [],
      "source": [
        "ncol(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9e8a4b1e-3862-4b7b-8ab1-8fc5681af050",
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      "outputs": [],
      "source": [
        "# Null values\n",
        "sum(is.na(df))"
      ]
    },
    {
      "cell_type": "code",
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      "id": "7d6e04a2-baa7-4b02-aec8-65ae3d1c5d9e",
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      },
      "outputs": [],
      "source": [
        "# Missing values per column\n",
        "print(sapply(df,function(x) sum(is.na(x))))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f22f49e7-8469-4fe4-b2ac-ccbb437b55c9",
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      "outputs": [],
      "source": [
        "#check the percentage of the missing values per column\n",
        "missing_percent <- colSums(is.na(df)) / nrow(df) * 0.01\n",
        "print(missing_percent)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4bb15a0d-0834-4e0d-a66d-e4510cb5aaaa",
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      },
      "outputs": [],
      "source": [
        "#if the percentage is low, replace the missing values with the mean of each\n",
        "#calculating the mean\n",
        "null_values <- colnames(df)[colSums(is.na(df)) > 0]\n",
        "means <- sapply(df[, null_values], mean, na.rm = TRUE)\n",
        "print(means)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "100648a9-0421-4e79-8130-4cf9a5aab3ab",
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      "outputs": [],
      "source": [
        "#replacing the missing values\n",
        "for (col in null_values) {\n",
        "  df[[col]][is.na(df[[col]])] <- means[col]\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
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      "id": "44a464c7-4552-457e-986b-34020c842dec",
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      "outputs": [],
      "source": [
        "print(sapply(df,function(x) sum(is.na(x))))"
      ]
    },
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      "outputs": [],
      "source": [
        "correlation <-cor(df)"
      ]
    },
    {
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      "id": "549679b0-a603-4b95-9c77-bf9df95dd4d3",
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      "outputs": [],
      "source": [
        "print(correlation)"
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      "source": [
        "# Correlation of all features with target column(portability)\n",
        "# Use of sapply to iterate through all the features except protability computing correlation against it.\n",
        "\n"
      ]
    },
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      "id": "cd06c154-6bbf-404e-ae2c-3ee8e00fe882",
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      },
      "outputs": [],
      "source": [
        "# # parameters<-list(df$ph,df$Hardness,df$Solids,df$Chloramines,df$Sulfate,df$Conductivity,df$Organic_carbon,df$Trihalomethanes,df$Turbidity)#list of all parameters except portability\n",
        "\n",
        "# target_var<-df$Potability\n",
        "# correlations <- sapply(parameters, function(x) cor(x, target_var)) #checking correlation of all other variables against portability                     \n",
        "                 \n",
        "                       \n",
        "# print(correlations)"
      ]
    },
    {
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      "id": "cd4b9b16-17e4-43d0-bb01-0650f313bb4b",
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      },
      "outputs": [],
      "source": [
        "#setting Potability as the target variable\n",
        "target_var <- df$Potability\n",
        "\n",
        "# Calculating correlations for all variables in the df against the target variable then storing results in a new data frame\n",
        "correlation_data <- data.frame(\n",
        "  Variable = colnames(df), \n",
        "  Correlation = sapply(df, function(x) {\n",
        "    corr_value <- cor(x, target_var)\n",
        "    round(corr_value, 5)\n",
        "  })\n",
        ")\n",
        "correlation_data <- correlation_data[order(correlation_data$Correlation, decreasing = TRUE), ]\n",
        "\n",
        "\n",
        "print(correlation_data)"
      ]
    },
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      "outputs": [],
      "source": [
        "# cor(df$ph,df$Hardness)\n"
      ]
    },
    {
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      "id": "8fb6e58b-ef94-4f33-a05a-2a57cd97758d",
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      },
      "outputs": [],
      "source": [
        "dups <- sum(duplicated(df))"
      ]
    },
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      "id": "f0984a3c-eccc-46e5-b6fd-19cdecd05959",
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        "print(dups)"
      ]
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      "outputs": [],
      "source": [
        "str(df)"
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      },
      "outputs": [],
      "source": [
        "val_counts <- table(df$Potability)\n",
        "print(val_counts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9e2a3664-0770-407e-9cb3-22e69a16bee2",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:04:51.906611+00:00",
          "start_time": "2023-11-16T15:04:51.895600+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "30c775f8-32b6-44a8-8f30-b30115e7e361"
        }
      },
      "outputs": [],
      "source": [
        "column_names <- colnames(df)\n",
        "print(column_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aa6b19f5-ea93-43b3-8b9a-697817ed3031",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:05:03.485694+00:00",
          "start_time": "2023-11-16T15:04:53.146563+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "cac4959f-18ee-4861-8981-9ffeeab9242c"
        }
      },
      "outputs": [],
      "source": [
        "install.packages(\"skimr\")\n",
        "library(skimr)\n",
        "# skim(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2113e546-ff87-4648-bd56-26206dbe146c",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:05:03.504680+00:00",
          "start_time": "2023-11-16T15:05:03.493297+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "63e2a7f8-f95f-4c32-bf5f-6c9bd109fe2d"
        }
      },
      "outputs": [],
      "source": [
        "# Check values(unique)\n",
        "table(df$Potability)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0c2f557c-0f3b-4f6d-bfa3-dd60aa8a1cb3",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:05:25.047966+00:00",
          "start_time": "2023-11-16T15:05:03.589545+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "b141864e-ab42-46d9-97e2-4e01fe33067b"
        },
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "install.packages('ggplot2')\n",
        "library(ggplot2)\n",
        "ggplot(df, aes(x=Hardness)) + geom_histogram(binwidth = 5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f2ad0aee-d248-464e-b14c-afd426e28a99",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:05:25.606553+00:00",
          "start_time": "2023-11-16T15:05:25.313654+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "69f795f3-bac1-41fa-a8ae-2905c9b6c252"
        }
      },
      "outputs": [],
      "source": [
        "ggplot(df, aes(x=ph)) + geom_histogram()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0e6ebdf7-f248-4899-8046-a8f42a595e3d",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:05:26.208166+00:00",
          "start_time": "2023-11-16T15:05:25.940380+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "31649fca-6747-4851-a1ba-36426a0fa1ea"
        }
      },
      "outputs": [],
      "source": [
        "# Distribution of portable and non portsble water values\n",
        "ggplot(df, aes(x = Potability, fill=Potability)) + geom_bar()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1e7181bb-8ff3-4068-b9b7-d58eeaefb9ff",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:05:36.347930+00:00",
          "start_time": "2023-11-16T15:05:26.466693+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "4bc24759-4155-4f63-b9d4-5ed0e4350c9b"
        }
      },
      "outputs": [],
      "source": [
        "install.packages('corrplot')\n",
        "library(corrplot)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8bec93bb-a442-428d-be88-57a8091e543f",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:05:36.510085+00:00",
          "start_time": "2023-11-16T15:05:36.381409+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "6a1fbb1d-0c42-4435-8db3-83f80950eb79"
        }
      },
      "outputs": [],
      "source": [
        "corr <- cor(df)\n",
        "if (any(is.na(corr)) || any(is.nan(corr))) {\n",
        " corr[is.na(corr)] <- 0  # Replace NAs with 0\n",
        "}\n",
        "print(class(corr))\n",
        "print(str(corr))\n",
        "rounded_corr <- round(corr, 2)\n",
        "\n",
        "corrplot(rounded_corr, method = 'number')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a76499bf-89b0-4d70-87fc-93d19ce9d58b",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:05:36.859923+00:00",
          "start_time": "2023-11-16T15:05:36.783967+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "4275e626-c0a0-43e3-addb-f2b6875b39d1"
        }
      },
      "outputs": [],
      "source": [
        "\n",
        "corrplot(corr, method = 'color', col = colorRampPalette(c(\"red\", \"white\", \"blue\"))(100), order = 'alphabet')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6f2477c5-2b97-4ce4-a1d7-37ce126ceea6",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:05:37.217876+00:00",
          "start_time": "2023-11-16T15:05:37.205557+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "3175ee5e-bbef-4f28-89dd-b7b58fee532b"
        }
      },
      "outputs": [],
      "source": [
        "# ggplot(df, aes(x=ph, y=Hardness)) + geom_point() +  ggtitle('Hardness against ph Scatterplot')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d03967de-d379-4054-9b43-61416be7471a",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:05:37.229069+00:00",
          "start_time": "2023-11-16T15:05:37.222374+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "55f63d8c-3336-4d86-8fd0-98a288c6ff29"
        }
      },
      "outputs": [],
      "source": [
        "# ggplot(df, aes(x=Solids, y=Sulfate)) + geom_point() + ggtitle('Sulfates against Solids Scatterplot')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b0843c0d-dfed-48cb-972a-fc520b7636d3",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:05:39.307411+00:00",
          "start_time": "2023-11-16T15:05:39.297129+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "c9c82348-bfa1-400e-8390-0b9c1d55a274"
        }
      },
      "outputs": [],
      "source": [
        "# ggplot(df, aes(x=Trihalomethanes, y=Organic_carbon)) + geom_point() + ggtitle('Trihalomethanes against Organic_carbon Scatterplot')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ae5db14a-816f-4d9d-8e55-e1d0b9f7a520",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:05:41.803876+00:00",
          "start_time": "2023-11-16T15:05:41.480784+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "5b02ac84-f55c-4ac0-9ea5-91349ddd2223"
        }
      },
      "outputs": [],
      "source": [
        "ggplot(df, aes(x=Potability, y=Hardness)) + geom_point() + ggtitle('Hardness against Potability Scatterplot')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6bbb269f-e0c1-41c8-bacc-bc5d6b5a01a2",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:05:43.296906+00:00",
          "start_time": "2023-11-16T15:05:43.051642+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "13372b4e-a5ba-4897-9863-016edb20e6b2"
        },
        "scrolled": false
      },
      "outputs": [],
      "source": [
        "ggplot(df, aes(x=ph)) + geom_boxplot() + ggtitle('ph Boxplot')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "54327e5f-f882-40d0-ae98-a158a52e672f",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:05:45.632751+00:00",
          "start_time": "2023-11-16T15:05:45.394498+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "7c4f6ecb-efe4-4de6-b3f6-e0503ad6cd62"
        }
      },
      "outputs": [],
      "source": [
        "ggplot(df, aes(x=Sulfate )) + geom_boxplot() + ggtitle('Sulfate Boxplot')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "adc90d96-32c8-4737-9db2-09a3269ebd93",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:06:10.980911+00:00",
          "start_time": "2023-11-16T15:05:52.343454+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "1666cfce-c207-497b-8661-c7b39d2118c1"
        }
      },
      "outputs": [],
      "source": [
        "# Outlier handling\n",
        "install.packages('dplyr')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4ec688bb-c1b4-4613-8d3a-531bbf840da3",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:06:12.698182+00:00",
          "start_time": "2023-11-16T15:06:12.641641+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "763502a1-27e2-4d7e-94ba-e6d7ac6f5109"
        }
      },
      "outputs": [],
      "source": [
        "library(dplyr)\n",
        "filtered <-filter(df,  ph >=0, ph<=14)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8cf03f2d-f929-401f-8132-32d6d49ecf5f",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:06:16.382994+00:00",
          "start_time": "2023-11-16T15:06:16.343672+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "a555ed22-1e8e-4fc5-a1f6-fa676832e5b9"
        }
      },
      "outputs": [],
      "source": [
        "# Filter hardness\n",
        "filtered <-filter(df,  Hardness >=100, Hardness<=300)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "033a6b7e-86b0-43cd-b6db-17203598d667",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:06:21.294274+00:00",
          "start_time": "2023-11-16T15:06:21.277563+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "ef34e507-2282-403f-bf97-ce3d1c76406f"
        }
      },
      "outputs": [],
      "source": [
        "# Filtered Sulphates\n",
        "filtered <-filter(df,  Sulfate >=200, Sulfate<=450)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f785a91b-629e-411b-98e1-5e3046d5f872",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:06:22.693202+00:00",
          "start_time": "2023-11-16T15:06:22.683006+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "1d5d05ec-1809-4b17-bee8-de05cb904a91"
        }
      },
      "outputs": [],
      "source": [
        "# # Max pH\n",
        "# max(filtered$Hardness)\n",
        "# min(filtered$Hardness)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8253e97b-a6d0-4699-a9e2-5f71586de1ad",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:06:24.487586+00:00",
          "start_time": "2023-11-16T15:06:24.437745+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "72d972a6-f9bc-41e5-883a-6dab98b874c1"
        }
      },
      "outputs": [],
      "source": [
        "head(filtered)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c1fc9496-4265-44d2-bf7f-04411c65de12",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:06:25.958476+00:00",
          "start_time": "2023-11-16T15:06:25.934457+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "b6fc7c2b-0c4e-45b2-9b84-98d00dbe9064"
        }
      },
      "outputs": [],
      "source": [
        "dim(filtered)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1b5f93d4-a43d-4ece-935c-6bced942aebc",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:06:40.688757+00:00",
          "start_time": "2023-11-16T15:06:29.285578+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "63041d14-d097-4548-9926-f8e2b88eed41"
        }
      },
      "outputs": [],
      "source": [
        "# Modeling\n",
        "install.packages('randomForest')"
      ]
    },
    {
      "id": "38516a0a-36b2-4736-9b72-02d7c4f86407",
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "3bbaddb4-743e-4caa-af47-f7767c704b5c"
        },
        "ExecuteTime": {
          "end_time": "2023-11-16T15:07:53.257089+00:00",
          "start_time": "2023-11-16T15:07:42.235817+00:00"
        }
      },
      "execution_count": null,
      "source": "install.packages(\"caTools\")\n",
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c9125773-da36-4aea-8a80-3316c50ca4fe",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:07:55.591158+00:00",
          "start_time": "2023-11-16T15:07:55.576439+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "1d5fd416-af45-414d-8906-f6ed4981d035"
        }
      },
      "outputs": [],
      "source": "library(randomForest)\n"
    },
    {
      "id": "687ce078-d1a9-4691-b3b3-49ac9d8d86b7",
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "15a785cd-ca94-46d7-88f1-f3a3f21c006a"
        },
        "ExecuteTime": {
          "end_time": "2023-11-16T15:07:58.093528+00:00",
          "start_time": "2023-11-16T15:07:58.081541+00:00"
        }
      },
      "execution_count": null,
      "source": "library(caTools) ",
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9570087a-5c90-4887-87ed-7ae68dd7782c",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:08:02.208758+00:00",
          "start_time": "2023-11-16T15:08:02.183692+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "5c839ccb-82e1-40a8-9009-02acbddbf294"
        }
      },
      "outputs": [],
      "source": "# Select target and features\nsplit <- sample.split(df, SplitRatio = 0.7) \nsplit"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0343d229-8293-4c18-a9c5-bea704b17ed7",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-11-16T15:08:07.620392+00:00",
          "start_time": "2023-11-16T15:08:07.609117+00:00"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
          "output_collection_id": "6b913d5e-578f-474e-a4fb-9c3ad33f5b43"
        }
      },
      "outputs": [],
      "source": "train <- subset(df, split == \"TRUE\") \ntest <- subset(df, split == \"FALSE\") \n\n"
    },
    {
      "id": "f47c3d97-460b-41a3-96ca-8e5860daaf4b",
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code",
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        },
        "ExecuteTime": {
          "end_time": "2023-11-16T15:08:09.686793+00:00",
          "start_time": "2023-11-16T15:08:09.672725+00:00"
        }
      },
      "execution_count": null,
      "source": "set.seed(120)  # Setting seed ",
      "outputs": []
    },
    {
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      "cell_type": "code",
      "metadata": {
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          "source_hidden": false
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        "noteable": {
          "cell_type": "code",
          "output_collection_id": "9c1a164d-ee8c-4a26-80d2-69afac633fb9"
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          "start_time": "2023-11-16T15:08:14.329992+00:00"
        }
      },
      "execution_count": null,
      "source": "# Example for classification\nclassifier_RF <- randomForest(x = train[-10], y = as.factor(train$Potability), ntree = 500)\n",
      "outputs": []
    },
    {
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      "cell_type": "code",
      "metadata": {
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          "source_hidden": false
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          "start_time": "2023-11-16T15:08:16.299752+00:00"
        }
      },
      "execution_count": null,
      "source": "classifier_RF",
      "outputs": []
    },
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      "cell_type": "code",
      "metadata": {
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          "source_hidden": false
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          "output_collection_id": "9067c3ba-4c1c-48f9-8b41-24f0a2d42b3f"
        },
        "ExecuteTime": {
          "end_time": "2023-11-16T15:08:18.213846+00:00",
          "start_time": "2023-11-16T15:08:17.908622+00:00"
        }
      },
      "execution_count": null,
      "source": "y_pred = predict(classifier_RF, newdata = test[-10]) \n",
      "outputs": []
    },
    {
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      "cell_type": "code",
      "metadata": {
        "jupyter": {
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          "source_hidden": false
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          "output_collection_id": "d99ee40f-3163-4948-8a0b-41c7f09666cb"
        },
        "ExecuteTime": {
          "end_time": "2023-11-16T15:08:21.018964+00:00",
          "start_time": "2023-11-16T15:08:21.009361+00:00"
        }
      },
      "execution_count": null,
      "source": "# plot(classifier_RF) \n",
      "outputs": []
    },
    {
      "id": "55cf00e7-7bea-4c4b-8e4b-31dbf7ace9c1",
      "cell_type": "code",
      "metadata": {
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          "source_hidden": false
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        "ExecuteTime": {
          "end_time": "2023-11-16T15:08:21.872079+00:00",
          "start_time": "2023-11-16T15:08:21.854709+00:00"
        }
      },
      "execution_count": null,
      "source": "confusion_mtx = table(test[, 10], y_pred) \nconfusion_mtx",
      "outputs": []
    },
    {
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      "cell_type": "code",
      "metadata": {
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        "ExecuteTime": {
          "end_time": "2023-11-16T15:08:24.334337+00:00",
          "start_time": "2023-11-16T15:08:24.315860+00:00"
        },
        "dx": {
          "dashboard": {
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          },
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              "chartMode": "grid",
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      },
      "execution_count": null,
      "source": "# Feature Importance\nimportance(classifier_RF) \n",
      "outputs": []
    },
    {
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      "cell_type": "code",
      "metadata": {
        "jupyter": {
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          "source_hidden": false
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          "end_time": "2023-11-16T15:14:43.838873+00:00",
          "start_time": "2023-11-16T15:14:43.789188+00:00"
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      },
      "execution_count": null,
      "source": "varImpPlot(classifier_RF) \n",
      "outputs": []
    },
    {
      "id": "899bb8d1-129d-42f8-a40a-15d606545606",
      "cell_type": "code",
      "metadata": {
        "jupyter": {
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          "source_hidden": false
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          "start_time": "2023-11-16T15:08:29.873338+00:00"
        }
      },
      "execution_count": null,
      "source": "# Using filtered data\n# Select target and features\nf_split <- sample.split(filtered, SplitRatio = 0.7) \nf_split",
      "outputs": []
    },
    {
      "id": "b2fbcad0-cb88-4313-a999-aa3204ecbba0",
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      },
      "execution_count": null,
      "source": "f_train <- subset(df, split == \"TRUE\") \nf_test <- subset(df, split == \"FALSE\") \n\n",
      "outputs": []
    },
    {
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          "start_time": "2023-11-16T15:11:18.045507+00:00"
        }
      },
      "execution_count": null,
      "source": "f_classifier_RF <- randomForest(x = f_train[-10], y = as.factor(f_train$Potability), ntree = 500)\n",
      "outputs": []
    },
    {
      "id": "dd85bfbf-722b-4563-a1a8-7ad7931a14a2",
      "cell_type": "code",
      "metadata": {
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      "execution_count": null,
      "source": "f_classifier_RF",
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    },
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      "cell_type": "code",
      "metadata": {
        "jupyter": {
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      },
      "execution_count": null,
      "source": "f_confusion_mtx = table(f_test[, 10], y_pred) \nf_confusion_mtx",
      "outputs": []
    },
    {
      "id": "05def48a-9874-4e3e-bbe0-357ad3476e95",
      "cell_type": "code",
      "metadata": {
        "jupyter": {
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          "output_collection_id": "3b0c899a-edae-4c40-85a0-e110fc83bf78"
        },
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          "start_time": "2023-11-16T15:13:22.805088+00:00"
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      },
      "execution_count": null,
      "source": "# Feature Importance\nimportance(f_classifier_RF) \n",
      "outputs": []
    },
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      "source": "varImpPlot(f_classifier_RF) \n",
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      "execution_count": null,
      "source": "write.csv(filtered, \"processed_data.csv\", row.names=FALSE)",
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