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ranking_and_filtering/daily/classification/Gender_Recognition_by_Voice_B4/Gender_Recognition_by_Voice_B4_002/current.csv

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ranking_and_filtering/daily/classification/Gender_Recognition_by_Voice_B4/Gender_Recognition_by_Voice_B4_002/info.json

@@ -0,0 +1,127 @@
+{
+    "name": "Gender Recognition by Voice",
+    "source": "https://www.kaggle.com/datasets/primaryobjects/voicegender/data",
+    "data_intro": "This database was created to identify a voice as male or female, based upon acoustic properties of the voice and speech. The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz ([human vocal range][2]).",
+    "is_splited": false,
+    "overall_size": 3168,
+    "train_size": 0,
+    "test_size": 0,
+    "c_classes": 1,
+    "n_classes": 20,
+    "cat_feature_intro": {
+        "label": "- **label**: male or female"
+    },
+    "num_feature_intro": {
+        "meanfreq": "- **meanfreq**: mean frequency (in kHz)",
+        "sd": "- **sd**: standard deviation of frequency",
+        "median": "- **median**: median frequency (in kHz)",
+        "Q25": "- **Q25**: first quantile (in kHz)",
+        "Q75": "- **Q75**: third quantile (in kHz)",
+        "IQR": "- **IQR**: interquantile range (in kHz)",
+        "skew": "- **skew**: skewness (see note in specprop description)",
+        "kurt": "- **kurt**: kurtosis (see note in specprop description)",
+        "sp.ent": "- **sp.ent**: spectral entropy",
+        "sfm": "- **sfm**: spectral flatness",
+        "mode": "![CART model][4]",
+        "centroid": "- **centroid**: frequency centroid (see specprop)",
+        "meanfun": "- **meanfun**: average of fundamental frequency measured across acoustic signal",
+        "minfun": "- **minfun**: minimum fundamental frequency measured across acoustic signal",
+        "maxfun": "- **maxfun**: maximum fundamental frequency measured across acoustic signal",
+        "meandom": "- **meandom**: average of dominant frequency measured across acoustic signal",
+        "mindom": "- **mindom**: minimum of dominant frequency measured across acoustic signal",
+        "maxdom": "- **maxdom**: maximum of dominant frequency measured across acoustic signal",
+        "dfrange": "- **dfrange**: range of dominant frequency measured across acoustic signal",
+        "modindx": "- **modindx**: modulation index. Calculated as the accumulated absolute difference between adjacent measurements of fundamental frequencies divided by the frequency range"
+    },
+    "evaluation_metric": null,
+    "task_type": "classification",
+    "target": "label",
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+            "male"
+        ]
+    }
+}

ranking_and_filtering/daily/classification/Gender_Recognition_by_Voice_B4/Gender_Recognition_by_Voice_B4_002/test_current.csv

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ranking_and_filtering/daily/classification/Gender_Recognition_by_Voice_B4/Gender_Recognition_by_Voice_B4_005/info.json

@@ -0,0 +1,127 @@
+{
+    "name": "Gender Recognition by Voice",
+    "source": "https://www.kaggle.com/datasets/primaryobjects/voicegender/data",
+    "data_intro": "This database was created to identify a voice as male or female, based upon acoustic properties of the voice and speech. The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz ([human vocal range][2]).",
+    "is_splited": false,
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+    "train_size": 0,
+    "test_size": 0,
+    "c_classes": 1,
+    "n_classes": 20,
+    "cat_feature_intro": {
+        "label": "- **label**: male or female"
+    },
+    "num_feature_intro": {
+        "meanfreq": "- **meanfreq**: mean frequency (in kHz)",
+        "sd": "- **sd**: standard deviation of frequency",
+        "median": "- **median**: median frequency (in kHz)",
+        "Q25": "- **Q25**: first quantile (in kHz)",
+        "Q75": "- **Q75**: third quantile (in kHz)",
+        "IQR": "- **IQR**: interquantile range (in kHz)",
+        "skew": "- **skew**: skewness (see note in specprop description)",
+        "kurt": "- **kurt**: kurtosis (see note in specprop description)",
+        "sp.ent": "- **sp.ent**: spectral entropy",
+        "sfm": "- **sfm**: spectral flatness",
+        "mode": "![CART model][4]",
+        "centroid": "- **centroid**: frequency centroid (see specprop)",
+        "meanfun": "- **meanfun**: average of fundamental frequency measured across acoustic signal",
+        "minfun": "- **minfun**: minimum fundamental frequency measured across acoustic signal",
+        "maxfun": "- **maxfun**: maximum fundamental frequency measured across acoustic signal",
+        "meandom": "- **meandom**: average of dominant frequency measured across acoustic signal",
+        "mindom": "- **mindom**: minimum of dominant frequency measured across acoustic signal",
+        "maxdom": "- **maxdom**: maximum of dominant frequency measured across acoustic signal",
+        "dfrange": "- **dfrange**: range of dominant frequency measured across acoustic signal",
+        "modindx": "- **modindx**: modulation index. Calculated as the accumulated absolute difference between adjacent measurements of fundamental frequencies divided by the frequency range"
+    },
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+    }
+}

ranking_and_filtering/daily/classification/Gender_Recognition_by_Voice_B4/Gender_Recognition_by_Voice_B4_005/test_current.csv

@@ -0,0 +1,16 @@
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ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_001/current.csv

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ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_001/info.json

@@ -0,0 +1,130 @@
+{
+    "name": "Mobile Price Classification",
+    "source": "https://www.kaggle.com/datasets/iabhishekofficial/mobile-price-classification/data",
+    "data_intro": "This dataset contain information about many mobiles anda variables about it,containning some relation between features of a mobile phone(eg:- RAM,Internal Memory etc) and its selling price.",
+    "is_splited": true,
+    "overall_size": 3000,
+    "train_size": 2000,
+    "test_size": 1000,
+    "c_classes": 0,
+    "n_classes": 22,
+    "cat_feature_intro": {},
+    "num_feature_intro": {
+        "id": "Unique identifier for each mobile phone",
+        "battery_power": "Total energy capacity of the battery in mAh",
+        "blue": "Whether the phone has Bluetooth (1: Yes, 0: No)",
+        "clock_speed": "Speed at which microprocessor executes instructions (in GHz)",
+        "dual_sim": "Whether the phone supports dual SIM (1: Yes, 0: No)",
+        "fc": "Front Camera megapixels",
+        "four_g": "Whether the phone has 4G capability (1: Yes, 0: No)",
+        "int_memory": "Internal memory in Gigabytes",
+        "m_dep": "Mobile depth in cm",
+        "mobile_wt": "Weight of the mobile phone in grams",
+        "n_cores": "Number of cores in the processor",
+        "pc": "Primary Camera megapixels",
+        "px_height": "Pixel resolution height",
+        "px_width": "Pixel resolution width",
+        "ram": "Random Access Memory in Megabytes. Bob wants to find out some relation between features of a mobile phone (e.g., RAM, Internal Memory, etc.) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.",
+        "sc_h": "Screen height of the mobile in cm",
+        "sc_w": "Screen width of the mobile in cm",
+        "talk_time": "Maximum time that a single battery charge will last when the phone is used continuously in hours",
+        "three_g": "Whether the phone has 3G capability (1: Yes, 0: No)",
+        "touch_screen": "Whether the phone has a touch screen (1: Yes, 0: No)",
+        "wifi": "Whether the phone has WiFi capability (1: Yes, 0: No)",
+        "price_range": "Price range of the mobile (0: Low, 1: Medium, 2: High, 3: Very High)"
+    },
+    "evaluation_metric": null,
+    "task_type": "classification",
+    "target": "price_range",
+    "num_feature_value": {
+        "battery_power": [
+            500.0,
+            1999.0
+        ],
+        "blue": [
+            0.0,
+            1.0
+        ],
+        "clock_speed": [
+            0.5,
+            3.0
+        ],
+        "dual_sim": [
+            0.0,
+            1.0
+        ],
+        "fc": [
+            0.0,
+            19.0
+        ],
+        "four_g": [
+            0.0,
+            1.0
+        ],
+        "id": [
+            1.0,
+            1000.0
+        ],
+        "int_memory": [
+            2.0,
+            64.0
+        ],
+        "m_dep": [
+            0.1,
+            1.0
+        ],
+        "mobile_wt": [
+            80.0,
+            200.0
+        ],
+        "n_cores": [
+            1.0,
+            8.0
+        ],
+        "pc": [
+            0.0,
+            20.0
+        ],
+        "price_range": [
+            0.0,
+            3.0
+        ],
+        "px_height": [
+            0.0,
+            1960.0
+        ],
+        "px_width": [
+            500.0,
+            1998.0
+        ],
+        "ram": [
+            256.0,
+            3998.0
+        ],
+        "sc_h": [
+            5.0,
+            19.0
+        ],
+        "sc_w": [
+            0.0,
+            18.0
+        ],
+        "talk_time": [
+            2.0,
+            20.0
+        ],
+        "three_g": [
+            0.0,
+            1.0
+        ],
+        "touch_screen": [
+            0.0,
+            1.0
+        ],
+        "wifi": [
+            0.0,
+            1.0
+        ]
+    },
+    "cat_feature_value": {}
+}

ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_001/test.csv

@@ -0,0 +1,201 @@
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ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_002/info.json

@@ -0,0 +1,130 @@
+{
+    "name": "Mobile Price Classification",
+    "source": "https://www.kaggle.com/datasets/iabhishekofficial/mobile-price-classification/data",
+    "data_intro": "This dataset contain information about many mobiles anda variables about it,containning some relation between features of a mobile phone(eg:- RAM,Internal Memory etc) and its selling price.",
+    "is_splited": true,
+    "overall_size": 3000,
+    "train_size": 2000,
+    "test_size": 1000,
+    "c_classes": 0,
+    "n_classes": 22,
+    "cat_feature_intro": {},
+    "num_feature_intro": {
+        "id": "Unique identifier for each mobile phone",
+        "battery_power": "Total energy capacity of the battery in mAh",
+        "blue": "Whether the phone has Bluetooth (1: Yes, 0: No)",
+        "clock_speed": "Speed at which microprocessor executes instructions (in GHz)",
+        "dual_sim": "Whether the phone supports dual SIM (1: Yes, 0: No)",
+        "fc": "Front Camera megapixels",
+        "four_g": "Whether the phone has 4G capability (1: Yes, 0: No)",
+        "int_memory": "Internal memory in Gigabytes",
+        "m_dep": "Mobile depth in cm",
+        "mobile_wt": "Weight of the mobile phone in grams",
+        "n_cores": "Number of cores in the processor",
+        "pc": "Primary Camera megapixels",
+        "px_height": "Pixel resolution height",
+        "px_width": "Pixel resolution width",
+        "ram": "Random Access Memory in Megabytes. Bob wants to find out some relation between features of a mobile phone (e.g., RAM, Internal Memory, etc.) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.",
+        "sc_h": "Screen height of the mobile in cm",
+        "sc_w": "Screen width of the mobile in cm",
+        "talk_time": "Maximum time that a single battery charge will last when the phone is used continuously in hours",
+        "three_g": "Whether the phone has 3G capability (1: Yes, 0: No)",
+        "touch_screen": "Whether the phone has a touch screen (1: Yes, 0: No)",
+        "wifi": "Whether the phone has WiFi capability (1: Yes, 0: No)",
+        "price_range": "Price range of the mobile (0: Low, 1: Medium, 2: High, 3: Very High)"
+    },
+    "evaluation_metric": null,
+    "task_type": "classification",
+    "target": "price_range",
+    "num_feature_value": {
+        "battery_power": [
+            500.0,
+            1999.0
+        ],
+        "blue": [
+            0.0,
+            1.0
+        ],
+        "clock_speed": [
+            0.5,
+            3.0
+        ],
+        "dual_sim": [
+            0.0,
+            1.0
+        ],
+        "fc": [
+            0.0,
+            19.0
+        ],
+        "four_g": [
+            0.0,
+            1.0
+        ],
+        "id": [
+            1.0,
+            1000.0
+        ],
+        "int_memory": [
+            2.0,
+            64.0
+        ],
+        "m_dep": [
+            0.1,
+            1.0
+        ],
+        "mobile_wt": [
+            80.0,
+            200.0
+        ],
+        "n_cores": [
+            1.0,
+            8.0
+        ],
+        "pc": [
+            0.0,
+            20.0
+        ],
+        "price_range": [
+            0.0,
+            3.0
+        ],
+        "px_height": [
+            0.0,
+            1960.0
+        ],
+        "px_width": [
+            500.0,
+            1998.0
+        ],
+        "ram": [
+            256.0,
+            3998.0
+        ],
+        "sc_h": [
+            5.0,
+            19.0
+        ],
+        "sc_w": [
+            0.0,
+            18.0
+        ],
+        "talk_time": [
+            2.0,
+            20.0
+        ],
+        "three_g": [
+            0.0,
+            1.0
+        ],
+        "touch_screen": [
+            0.0,
+            1.0
+        ],
+        "wifi": [
+            0.0,
+            1.0
+        ]
+    },
+    "cat_feature_value": {}
+}

ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_002/test.csv

@@ -0,0 +1,201 @@
+battery_power,blue,clock_speed,dual_sim,fc,four_g,int_memory,m_dep,mobile_wt,n_cores,pc,px_height,px_width,ram,sc_h,sc_w,talk_time,three_g,touch_screen,wifi,price_range
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ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_002/test_current.csv

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ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_003/current.csv

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ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_003/info.json

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+{
+    "name": "Mobile Price Classification",
+    "source": "https://www.kaggle.com/datasets/iabhishekofficial/mobile-price-classification/data",
+    "data_intro": "This dataset contain information about many mobiles anda variables about it,containning some relation between features of a mobile phone(eg:- RAM,Internal Memory etc) and its selling price.",
+    "is_splited": true,
+    "overall_size": 3000,
+    "train_size": 2000,
+    "test_size": 1000,
+    "c_classes": 0,
+    "n_classes": 22,
+    "cat_feature_intro": {},
+    "num_feature_intro": {
+        "id": "Unique identifier for each mobile phone",
+        "battery_power": "Total energy capacity of the battery in mAh",
+        "blue": "Whether the phone has Bluetooth (1: Yes, 0: No)",
+        "clock_speed": "Speed at which microprocessor executes instructions (in GHz)",
+        "dual_sim": "Whether the phone supports dual SIM (1: Yes, 0: No)",
+        "fc": "Front Camera megapixels",
+        "four_g": "Whether the phone has 4G capability (1: Yes, 0: No)",
+        "int_memory": "Internal memory in Gigabytes",
+        "m_dep": "Mobile depth in cm",
+        "mobile_wt": "Weight of the mobile phone in grams",
+        "n_cores": "Number of cores in the processor",
+        "pc": "Primary Camera megapixels",
+        "px_height": "Pixel resolution height",
+        "px_width": "Pixel resolution width",
+        "ram": "Random Access Memory in Megabytes. Bob wants to find out some relation between features of a mobile phone (e.g., RAM, Internal Memory, etc.) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.",
+        "sc_h": "Screen height of the mobile in cm",
+        "sc_w": "Screen width of the mobile in cm",
+        "talk_time": "Maximum time that a single battery charge will last when the phone is used continuously in hours",
+        "three_g": "Whether the phone has 3G capability (1: Yes, 0: No)",
+        "touch_screen": "Whether the phone has a touch screen (1: Yes, 0: No)",
+        "wifi": "Whether the phone has WiFi capability (1: Yes, 0: No)",
+        "price_range": "Price range of the mobile (0: Low, 1: Medium, 2: High, 3: Very High)"
+    },
+    "evaluation_metric": null,
+    "task_type": "classification",
+    "target": "price_range",
+    "num_feature_value": {
+        "battery_power": [
+            500.0,
+            1999.0
+        ],
+        "blue": [
+            0.0,
+            1.0
+        ],
+        "clock_speed": [
+            0.5,
+            3.0
+        ],
+        "dual_sim": [
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+            1.0
+        ],
+        "fc": [
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+        "four_g": [
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+        "id": [
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+        ],
+        "int_memory": [
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+        ],
+        "m_dep": [
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+            1.0
+        ],
+        "mobile_wt": [
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+            200.0
+        ],
+        "n_cores": [
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+            8.0
+        ],
+        "pc": [
+            0.0,
+            20.0
+        ],
+        "price_range": [
+            0.0,
+            3.0
+        ],
+        "px_height": [
+            0.0,
+            1960.0
+        ],
+        "px_width": [
+            500.0,
+            1998.0
+        ],
+        "ram": [
+            256.0,
+            3998.0
+        ],
+        "sc_h": [
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+            19.0
+        ],
+        "sc_w": [
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+            18.0
+        ],
+        "talk_time": [
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+            20.0
+        ],
+        "three_g": [
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+            1.0
+        ],
+        "touch_screen": [
+            0.0,
+            1.0
+        ],
+        "wifi": [
+            0.0,
+            1.0
+        ]
+    },
+    "cat_feature_value": {}
+}

ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_003/test.csv

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ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_003/test_current.csv

@@ -0,0 +1,16 @@
+battery_power,blue,clock_speed,dual_sim,fc,four_g,int_memory,m_dep,mobile_wt,n_cores,pc,px_height,px_width,ram,sc_h,sc_w,talk_time,three_g,touch_screen,wifi,price_range
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ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_004/info.json

@@ -0,0 +1,130 @@
+{
+    "name": "Mobile Price Classification",
+    "source": "https://www.kaggle.com/datasets/iabhishekofficial/mobile-price-classification/data",
+    "data_intro": "This dataset contain information about many mobiles anda variables about it,containning some relation between features of a mobile phone(eg:- RAM,Internal Memory etc) and its selling price.",
+    "is_splited": true,
+    "overall_size": 3000,
+    "train_size": 2000,
+    "test_size": 1000,
+    "c_classes": 0,
+    "n_classes": 22,
+    "cat_feature_intro": {},
+    "num_feature_intro": {
+        "id": "Unique identifier for each mobile phone",
+        "battery_power": "Total energy capacity of the battery in mAh",
+        "blue": "Whether the phone has Bluetooth (1: Yes, 0: No)",
+        "clock_speed": "Speed at which microprocessor executes instructions (in GHz)",
+        "dual_sim": "Whether the phone supports dual SIM (1: Yes, 0: No)",
+        "fc": "Front Camera megapixels",
+        "four_g": "Whether the phone has 4G capability (1: Yes, 0: No)",
+        "int_memory": "Internal memory in Gigabytes",
+        "m_dep": "Mobile depth in cm",
+        "mobile_wt": "Weight of the mobile phone in grams",
+        "n_cores": "Number of cores in the processor",
+        "pc": "Primary Camera megapixels",
+        "px_height": "Pixel resolution height",
+        "px_width": "Pixel resolution width",
+        "ram": "Random Access Memory in Megabytes. Bob wants to find out some relation between features of a mobile phone (e.g., RAM, Internal Memory, etc.) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.",
+        "sc_h": "Screen height of the mobile in cm",
+        "sc_w": "Screen width of the mobile in cm",
+        "talk_time": "Maximum time that a single battery charge will last when the phone is used continuously in hours",
+        "three_g": "Whether the phone has 3G capability (1: Yes, 0: No)",
+        "touch_screen": "Whether the phone has a touch screen (1: Yes, 0: No)",
+        "wifi": "Whether the phone has WiFi capability (1: Yes, 0: No)",
+        "price_range": "Price range of the mobile (0: Low, 1: Medium, 2: High, 3: Very High)"
+    },
+    "evaluation_metric": null,
+    "task_type": "classification",
+    "target": "price_range",
+    "num_feature_value": {
+        "battery_power": [
+            500.0,
+            1999.0
+        ],
+        "blue": [
+            0.0,
+            1.0
+        ],
+        "clock_speed": [
+            0.5,
+            3.0
+        ],
+        "dual_sim": [
+            0.0,
+            1.0
+        ],
+        "fc": [
+            0.0,
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+        ],
+        "four_g": [
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+        ],
+        "id": [
+            1.0,
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+        ],
+        "int_memory": [
+            2.0,
+            64.0
+        ],
+        "m_dep": [
+            0.1,
+            1.0
+        ],
+        "mobile_wt": [
+            80.0,
+            200.0
+        ],
+        "n_cores": [
+            1.0,
+            8.0
+        ],
+        "pc": [
+            0.0,
+            20.0
+        ],
+        "price_range": [
+            0.0,
+            3.0
+        ],
+        "px_height": [
+            0.0,
+            1960.0
+        ],
+        "px_width": [
+            500.0,
+            1998.0
+        ],
+        "ram": [
+            256.0,
+            3998.0
+        ],
+        "sc_h": [
+            5.0,
+            19.0
+        ],
+        "sc_w": [
+            0.0,
+            18.0
+        ],
+        "talk_time": [
+            2.0,
+            20.0
+        ],
+        "three_g": [
+            0.0,
+            1.0
+        ],
+        "touch_screen": [
+            0.0,
+            1.0
+        ],
+        "wifi": [
+            0.0,
+            1.0
+        ]
+    },
+    "cat_feature_value": {}
+}

ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_004/test.csv

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ranking_and_filtering/daily/classification/Mobile_Price_Classification_B4/Mobile_Price_Classification_B4_005/info.json

@@ -0,0 +1,130 @@
+{
+    "name": "Mobile Price Classification",
+    "source": "https://www.kaggle.com/datasets/iabhishekofficial/mobile-price-classification/data",
+    "data_intro": "This dataset contain information about many mobiles anda variables about it,containning some relation between features of a mobile phone(eg:- RAM,Internal Memory etc) and its selling price.",
+    "is_splited": true,
+    "overall_size": 3000,
+    "train_size": 2000,
+    "test_size": 1000,
+    "c_classes": 0,
+    "n_classes": 22,
+    "cat_feature_intro": {},
+    "num_feature_intro": {
+        "id": "Unique identifier for each mobile phone",
+        "battery_power": "Total energy capacity of the battery in mAh",
+        "blue": "Whether the phone has Bluetooth (1: Yes, 0: No)",
+        "clock_speed": "Speed at which microprocessor executes instructions (in GHz)",
+        "dual_sim": "Whether the phone supports dual SIM (1: Yes, 0: No)",
+        "fc": "Front Camera megapixels",
+        "four_g": "Whether the phone has 4G capability (1: Yes, 0: No)",
+        "int_memory": "Internal memory in Gigabytes",
+        "m_dep": "Mobile depth in cm",
+        "mobile_wt": "Weight of the mobile phone in grams",
+        "n_cores": "Number of cores in the processor",
+        "pc": "Primary Camera megapixels",
+        "px_height": "Pixel resolution height",
+        "px_width": "Pixel resolution width",
+        "ram": "Random Access Memory in Megabytes. Bob wants to find out some relation between features of a mobile phone (e.g., RAM, Internal Memory, etc.) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.",
+        "sc_h": "Screen height of the mobile in cm",
+        "sc_w": "Screen width of the mobile in cm",
+        "talk_time": "Maximum time that a single battery charge will last when the phone is used continuously in hours",
+        "three_g": "Whether the phone has 3G capability (1: Yes, 0: No)",
+        "touch_screen": "Whether the phone has a touch screen (1: Yes, 0: No)",
+        "wifi": "Whether the phone has WiFi capability (1: Yes, 0: No)",
+        "price_range": "Price range of the mobile (0: Low, 1: Medium, 2: High, 3: Very High)"
+    },
+    "evaluation_metric": null,
+    "task_type": "classification",
+    "target": "price_range",
+    "num_feature_value": {
+        "battery_power": [
+            500.0,
+            1999.0
+        ],
+        "blue": [
+            0.0,
+            1.0
+        ],
+        "clock_speed": [
+            0.5,
+            3.0
+        ],
+        "dual_sim": [
+            0.0,
+            1.0
+        ],
+        "fc": [
+            0.0,
+            19.0
+        ],
+        "four_g": [
+            0.0,
+            1.0
+        ],
+        "id": [
+            1.0,
+            1000.0
+        ],
+        "int_memory": [
+            2.0,
+            64.0
+        ],
+        "m_dep": [
+            0.1,
+            1.0
+        ],
+        "mobile_wt": [
+            80.0,
+            200.0
+        ],
+        "n_cores": [
+            1.0,
+            8.0
+        ],
+        "pc": [
+            0.0,
+            20.0
+        ],
+        "price_range": [
+            0.0,
+            3.0
+        ],
+        "px_height": [
+            0.0,
+            1960.0
+        ],
+        "px_width": [
+            500.0,
+            1998.0
+        ],
+        "ram": [
+            256.0,
+            3998.0
+        ],
+        "sc_h": [
+            5.0,
+            19.0
+        ],
+        "sc_w": [
+            0.0,
+            18.0
+        ],
+        "talk_time": [
+            2.0,
+            20.0
+        ],
+        "three_g": [
+            0.0,
+            1.0
+        ],
+        "touch_screen": [
+            0.0,
+            1.0
+        ],
+        "wifi": [
+            0.0,
+            1.0
+        ]
+    },
+    "cat_feature_value": {}
+}